Int J Digit Libr (2005) 5: 42–56 / Digital Object Identifier (DOI) 10.1007/s00799-004-0108-6

Anevaluation of semantic fisheye viewsfor opportunistic search in an annotated image collection

Paul Janecek, Pearl Pu

Human Computer Interaction Group, I&C-IFF, Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerlande-mail: {paul.janecek,pearl.pu}@epfl.chPublished online: 15 February 2005 – Springer-Verlag 2005

Abstract. Visual interfaces are potentially powerfultools for users to explore a representation of a collec-tion and opportunistically discover information that willguide them toward relevant documents. Semantic fish-eye views (SFEVs) are focus + context visualizationtechniques that manage visual complexity by selectivelyemphasizing and increasing the detail of information re-lated to the user’s focus and deemphasizing or filteringless important information.In this paper we describe a prototype for visualizing

an annotated image collection and an experiment to com-pare the effectiveness of two distinctly different SFEVsfor a complex opportunistic search task. The first SFEVcalculates relevance based on keyword-content similar-ity and the second based on conceptual relationships be-tween images derived using WordNet. The results of theexperiment suggest that semantic-guided search is sig-nificantly more effective than similarity-guided search fordiscovering and using domain knowledge in a collection.

Keywords: Context+focus techniques – Semantic fish-eye views – Visual information retrieval interfaces – Em-pirical user study

1 Introduction

Search is often iterative, interactive, and opportunistic;analyzing the results of a query often leads to the discov-ery of unfamiliar vocabulary and relationships that guidethe future direction of search. Researchers have identi-fied a wide range of organizational structures [11, 25] andstrategies [1] useful for effective information seeking. Inthis paper we present an experiment to evaluate an in-teractive visualization technique that visually integratesthese structures and strategies into the interface to guidea user over a collection of information.

Visual information retrieval interfaces (VIRI) use in-formation visualization techniques to reveal relationshipsbetween documents and allow a user to rapidly shift be-tween search and browsing tasks [9]. The tight couplingbetween visualization and interaction make VIRIs poten-tially powerful tools for discovering global relationshipsbetween documents. However, there are several propertiesof visual representations that limit their effectiveness foropportunistic search. First, as the amount and complex-ity of information accessible in a VIRI grows, it is increas-ingly difficult to represent all of it in the limited space ofa display. A visualization is often a compromise betweenshowing a small amount of information in detail or a largeamount of information abstractly. Second, within a densedisplay of information, it is often difficult for users to see orvisually distinguish the subset that is immediately usefulfor their current task. Selecting or navigating to each itemto view detailed information incurs a cost in time and cog-nitive effort. Third, any single visual representation opti-mally supports only a small set of tasks [5, 28]. Supportinga diverse set of tasks, such as comparing content and struc-tural and temporal relationships between documents, willoften requiremultiple representations.Semantic fisheye views (SFEVs) are interactive fo-

cus + context visualization techniques that are designedto address these limitations by monitoring and reveal-ing the relative importance of information for the user’scurrent task [8]. SFEVs emphasize or increase the de-tail of the most important information and deempha-size or filter less important information [13]. This is use-ful for both selecting the information to represent inthe constraints of a single display and reducing the vi-sual complexity within a dense visualization. Further-more, by monitoring relative importance in multiple con-texts, these techniques can interactively reveal semanticor structural relationships that may not be visible in theoriginal representation.

P. Janecek, P. Pu: An evaluation of semantic fisheye views 43

We have developed a prototype that uses SFEV tech-niques to support a number of different opportunisticsearch strategies within a large collection of professionallyannotated images. The first technique emphasizes imagesand keywords that are similar in content to the focus, andthe second technique emphasizes information that is con-ceptually related to the focus based on WordNet, a gen-eral lexical ontology of the English language [17]. Thesedifferent SFEVs correspond to classic search strategiesused for opportunistic search over heterogeneous collec-tions of information [1, 2]. The prototype demonstrateshow SFEVs can be used to support a variety of differentsearch strategies.We have conducted an experimental evaluation com-

paring the effectiveness of similarity and semantic met-rics for complex, iterative search tasks. The initial resultsof the experiment suggest that semantic-guided searchis more effective than similarity-guided search for thesetasks.In the following section we briefly introduce focus +

context visualization techniques. We then describe ourSFEV prototype, which uses similarity and semantic re-lationships to guide search over an annotated image col-lection. The framework this prototype is based on is inde-pendent of any particular representation and coordinatesthe multiple interest metrics and emphasis techniquesthat create the fisheye view effect. We then discuss thedesign and results of the experimental evaluation.

2 Related work

2.1 Focus + context techniques

Furnas first described fisheye views as a technique for se-lectively reducing the information in a display to showthe most interesting items, where the degree of interest(DOI) was calculated as a tradeoff between a priori im-portance (global context) and relevance to the user’s cur-rent task [8]. Furnas suggested that this general techniquecould be used to create compact views in a variety of dif-ferent domains by redefining the function that calculatesDOI.Since then, researchers have developed a wide range

of fisheye view or focus + context interactive visualiza-tion techniques. Distortion techniques [15] use geometrictransforms to magnify the area spatially near the focusin the view. Graphical fisheye views [26] achieve similareffects by increasing the size or detail of information re-lated to the focus within the structure and constraintsof a graphical model. The effectiveness of both distor-tion techniques and graphical fisheye views for complexsearch tasks depends largely on whether distance withinthe view or graphical model corresponds to the needs ofthe user.Semantic fisheye views , on the other hand, are in-

dependent of a particular visual representation and cal-

culate conceptual distance from the focus within one ormore related data models [13]. Several other researchersare investigating focus + context techniques that arebased on structural or semantic relationships rather thana particular view. For example, ScentTrails [19] calcu-lates the DOI of objects in a Web page using a model ofInformation Scent [6, 21] and then highlights relevant in-formation by modifying the underlying HTML.

2.2 Exploring image collections

We refer to opportunistic search as an iterative, inter-active process that evolves in response to the informa-tion found and that encompasses both directed searchand browsing [2]. Browsing behaviors with different goals,strategies, and at different levels of granularity are a partof this process [16]. Furthermore, the results of informa-tion seeking are not limited to documents but also includethe knowledge accumulated during the search process [2].This knowledge is essential for understanding and usingthe information discovered during search [25].Several researchers have investigated visualizing image

collections in maps based on visual features such as color,texture, and shape [20, 24]. Our prototype uses a simi-lar layout but positions images based on text annota-tions. In a study of the effectiveness of these layouts forsearch, Rodden et al. found that users preferred orga-nizing and searching for images based on keywords [24].PhotoMesa [3] organizes collections of images hierarchi-cally by their metadata and visualizes them in densespace-filling maps. Users can rapidly browse over theimages using sophisticated zooming techniques. A sig-nificant difference between this and the SFEV approachis that the zoom effect is not applied uniformly overthe entire collection but rather to each image separatelybased on a measure of its relevance. This allows a userto rapidly see and access images that are not spatiallynear each other. Another very significant difference isthat our approach integrates a semantic model directlyinto browsing.Other researchers have also developed prototypes that

allow semantic search and browsing. Hollink et al. [10]recently developed a prototype that integrates four on-tologies for searching within and annotating an image col-lection. Yee et al. [27] developed and evaluated a browserthat organized an image collection by concept hierarchiesand allowed users to interactively navigate over the net-work of relationships between them. Both of these pro-totypes are hypertext browsers. Our prototype, on theother hand, uses visualization and interaction techniquesto allow users to explore semantic relationships. In thecomparitive study of browsing techniques conducted byYee et al., they found that users strongly preferred thissemantic search to a more classic keyword-based search.The experiment that we describe in this paper also com-pares keyword-based and semantic browsing, but withinthe same type of interactive visual environment.

44 P. Janecek, P. Pu: An evaluation of semantic fisheye views

3 A semantic fisheye view prototype

To evaluate the effectiveness of SFEVs for opportunis-tic search, we have developed a prototype that allowsa user to interactively explore a large collection of imagesand keywords using two different types of search strate-gies. The first SFEV is based on the similarity and directrelationships between objects in the collection, which cor-responds to traditional information retrieval search andbrowsing behavior. The second SFEV uses WordNet tofind images and keywords that are conceptually related tothe focus. When the user’s focus changes, the prototyperecalculates the DOI of related objects using a combina-tion of different metrics and then updates their represen-tation using visual emphasis techniques.The prototype allows a user to search for images using

keyword-based and concept-based queries and rapidlybrowse over the results by simply brushing over the visu-ali representation of images and their keywords. As thefocus changes, the interface calculates the relative inter-est of all objects in the workspace and smoothly animateschanges in their representation. The cost of accessinginformation in the local workspace is much lower thanquery-based interaction, which encourages opportunisticsearch and exploration. In the following sections we de-scribe the prototype in greater detail.

3.1 Software architecture

The prototype is a Java application that integrates threemain software components: two packages that manage allqueries, visualization, and interaction with the image andWordNet databases, and VisAmp, a general library thatmanages the interest metrics and emphasis techniquesused by SFEVs. The VisAmp library was originally de-veloped as part of a prototype for browsing tabular infor-mation [13, 23] but is independent of the representationand datasets. We based parts of the architecture on sev-eral abstract classes used for general layout and displayof graphs in the graphview library originally developed forthe KAON project [18].

3.2 The Corbis image collection

The prototype allows the user to browse over a diverse col-lection of over 56 500 images that Corbis Corp. made ac-cessible to us for this research.The images in the collectioncover a wide range of subjects and are very well annotatedwith a rich vocabulary of over 28 000 unique keywords.Each image in the collection was professionally annotatedby Corbis with keywords describing various aspects of theimage, such as people, animals, objects, actions, mood,and location. Each image has an average of 23 keywords,which is sufficient to enable use of traditional informationretrieval techniques with fewmodifications.An important property of the keyword annotations

is that they were produced with the aid of a propri-

etary thesaurus that is not distributed with the images.This improves the quality of the annotations by ensuringa controlled vocabulary and expands each concept withboth a limited number of common synonyms. In add-ition, the thesaurus allows the annotators to add a listof related keywords that represent the path upward toprogressively more general concepts. From a search per-spective, this allows a user to find an image when theirsearch was for a more general concept. From a browsingperspective, this allows a user to learn the vocabulary ofthe classification hierarchy and navigate to more generalconcepts.

3.3 Visual layout

We use a spring layout to position the images and key-words in the workspace by modeling both types of objectsas nodes in a graph and the relationships between themas edges. The importance of a keyword in an image is as-signed to the weight of the edge connecting them. A singlekeyword may be connected to multiple images, and thereare no direct connections between images or between key-words. The spring layout tends to place highly connectedsets of nodes in clusters, and minimally connected nodesare moved to the outside of the visualization.The implementation of the spring layout is adapted

from the graphview package of the KAON library [18].In general, a spring algorithm assigns a repulsive forceto the nodes in a graph and models the edges betweennodes as springs with a tension based on their weight.To reduce empty space in the layout, we separated therepulsive force of each node into x and y componentsthat varied according to the image or keyword dimen-sions. Furthermore, to increase the legibility of the key-words, we minimized the vertical overlap by exaggeratingtheir y component of repulsion. The algorithm initiallyplaces nodes randomly and then iteratively attempts tominimize tension. The resulting layout tends to placehighly connected sets of nodes into clusters and mini-mally connected nodes toward the outside of the repre-sentation. Figure 1 shows the effect of applying this al-gorithm to a query for the concept “mares.” Words thatare often used in common will tend to be positioned neareach other, e.g., “colt” and “foal” in the bottom rightof the figure or “maternal” and “motherhood” in thetop left.

3.4 Semantic fisheye views

Interaction with the image collection is based on ourSFEV framework [13]. We use the following general equa-tion to calculate the DOI:

DOIcontext(x|fp= y) =API(x)+n∑

i=1

widisti(x, y) . (1)

This equation identifies two components that influ-ence the DOI assigned to each object, x , in the interface

P. Janecek, P. Pu: An evaluation of semantic fisheye views 45

Fig. 1. Images and keywords loaded into the workspace arepositioned using a spring layout algorithm. The visualweight of the images reflects their relevance toa query for the “mare, female horse” concept1

given the current focus, fp = y. The first is a priori in-terest (API), which models the relative importance of ob-jects independently of user focus. Objects with high APIare landmarks in the information collection. For example,“mares” and “horses” are shown prominently in Fig. 1.The second component models the importance of objectswith respect to the current focus and is the result of oneor more interest metrics that measure the distance be-tween each object and the focus in a dimension of interest,dist(x, y). The relative contribution of interest metricscan be adjusted with a weight, w . The equation can beadapted to model different contexts of interest, such askeyword or conceptual similarity, by modifying the com-ponents. Each distance metric calculates relative interestin a different way, and potentially within different se-mantic models. We use different combinations of interestmetrics to support the two different browsing techniquesin the prototype.

3.5 Similarity-guided browsing

The first set of interest metrics are designed to reveal in-formation in the collection that is similar to the currentfocus based on content. We derive the similarity betweenkeywords and images from the order in which keywordsare used to annotate an image. This heuristic obviouslydepends on how the images are annotated but generallyworks well in this collection as an indirect result of howCorbis uses a thesaurus to create their annotations.

1 The color version of this paper is available athttp://hci.epfl.ch/publications/2004/janecek-jdl104.pdf.

Figure 2 shows a model of similarity-guided searchwhen a keyword is the focus. The data state model tracesthe flow of information from the user’s interaction on thebottom right through the similarity metric and back tothe updated view. Each node in the model is a distinctstate of the data, and the edges represent transforms be-tween states. The model is divided into three verticalregions representing different stages in creating a visual-ization: the Image Collection, the Graphical Model , andthe View . When the user focuses on the representationof a keyword node in the view, the prototype traces thenode back to the underlying keyword object in the imagecollection and passes this to the similarity metric.The links from the keyword to related images are then

expanded, and this collection is limited to an ordered setof the most important ones, based on the weight of thelinks between the images and keywords. The distributionof DOI values is distorted and scaled to increase contrastand then passed to a function that animates changes inthe DOI of objects in the collection. Emphasis techniquesdepend on the DOI value to determine the visual weightto render objects, so any number of visual properties canbe automatically coordinated in this way. Figure 3 showsthe similarity value calculated using this metric for animage to all of its keywords. The degree of interest isshown using size and saturation in the representation ofthe images, keywords, and edges.A similar process is followed to calculate the similar-

ity between images, as shown in Fig. 4, with the resultingscreenshots shown in Fig. 5.When the limits of these met-rics are changed, the prototype can be configured to pro-gressively add information to the display in descendingorder of DOI.The metrics discussed above reveal similarities be-

tween objects and create visual clusters of tightly con-nected nodes. When the user changes focus, the inter-face recalculates the DOI of every object to reflect theirsimilarity to the new focus. This supports opportunis-tic discovery of images that are similarly annotated andthe keywords that are used in the vocabulary of the col-

Fig. 2. Calculating the similarity between keywords and images

46 P. Janecek, P. Pu: An evaluation of semantic fisheye views

Fig. 3. Representing the similarity between an image and itsrelated keywords

Fig. 4. Calculating the similarity between images

lection. In the following section, we describe metrics forfinding information that is conceptually related but notsimilarly annotated.

3.6 Semantic-guided browsing

The second type of SFEV implemented in the prototypeallows a user to search and browse over the image collec-tion using the semantic relationships modeled in Word-Net. In the next section we give a brief overview of Word-Net and then describe our implementation.

Fig. 5. Representing the similarity between images. The topscreenshot uses image size to indicate similarity. Thebottom screenshot adds a small number of important

keywords relating the images

3.6.1 WordNet

WordNet is a general lexical ontology originally de-veloped at Princeton University in 1985 to test psycholin-guistic theories of the English language [17].WordNet hascontinued to evolve since then, and the version this re-search is based on (1.7.1, August 2002) contains approxi-mately 140000 unique word forms with 111000 differentsenses. Only the base forms of words are stored in theWordNet database and are referred to as lemmas. Thedifferent senses of words (i.e., concepts) are modeled as

P. Janecek, P. Pu: An evaluation of semantic fisheye views 47

Fig. 6. Correspondence between the hierarchical annotation inCorbis and the hyponym generalization hierarchy in WordNet [14]

sets of synonyms and are referred to in WordNet’s doc-umentation as synsets. These concepts and lemmas areorganized into a network using a set of semantic and lexi-cal relationships.The overlap between the thesaurus used to annotate

images in the Corbis collection and the hierarchies inWordNet enables us to derive relationships between thekeywords in Corbis and the concepts in WordNet with anaccuracy of over 90% for the most frequently occurringkeywords [14]. Figure 6 shows an example of the overlapbetween the annotations of an image of an Arabian horseand the generalization hierarchy in WordNet.

3.6.2 Semantic interest metrics

Our implementation of semantic-guided browsing isbased on several of the search tactics identified by Batesthat people use to redirect their search to more generalor more specific concepts when they find too many or toofew results [1].Detailed semantic structions such as WordNet are of-

ten difficult for novices to use for a number of reasons,e.g., their specialized vocabularies, internal inconsisten-cies, incomplete structure, or level of granularity. Figure 7shows a portion of the overlapping generalization andcomposition hierarchies for the concept “horse.” For ex-ample, a person looking for “foal” and “mare” might be

Fig. 7. A portion of WordNet showing the overlappinggeneralization and composition hierarchies

surprised that they are not related to “horse” in the sameway (region 1 ), or a person looking for the substance ofa horse’s hoof might be unable to find the concept wherethis information is encoded (region 2 ). To simplify theprocess of browsing usingWordNet, we combine the threestrategies to show the local neighborhood around the cur-rent focus.We use a single complex composite metric to simul-

taneously support three basic search strategies: SUPER,SUB, and SIBLING. Each of these strategies is modeledas a directed search in WordNet along a particular typeof relationship. Figure 8 shows examples of the seman-tic neighborhood that could be found using each of thesestrategies. For example, if a search for “horse” returnedtoo many images, a person could use the SUB strategyto find a narrower set of more specific concepts, such asimages of “wild horse” or “foal.” In order to apply thesestrategies without access to a semantic model such asWordNet, a person would have to know the existence ofmore general, more specific, or related concepts and addthem to the query. Implementing these strategies usingSFEV techniques allows a user to simply brush over a key-word and see the related concepts that exist in the imagecollection.Figure 9 shows a data state model tracing the flow

of information in a composite semantic metric where theuser is focusing on a keyword. This model is similar to themodels in Figs. 2 and 4, with the addition of a vertical re-gion on the left that represents the subgraph of WordNetloaded into the browser. This subgraph contains all theconcepts directly and indirectly related to the keywordsloaded into the workspace.When a user brushes over a keyword in the graphical

model, the keyword is expanded to the different senses ofthe word. The composite metric expands the neighbor-

Fig. 8. A portion of the semantic neighborhood of the “horse”concept based on the SUPER, SUB, and SIBLING strategies

48 P. Janecek, P. Pu: An evaluation of semantic fisheye views

Fig. 9. Calculating interest with the SUPER, SUB, and SIBLINGstrategies using a composite interest metric

hood around each of these senses using the SUPER, SIB-LING, and SUB strategies in parallel. The results of thesemetrics are a set of related concepts weighted by theirdistance from the focus. Each of these concepts is thenmapped to a set of related images and keywords (C∗⇒ I).The set of images andkeywords foundwithin each strategyis assigned a DOI based on the path distance to the focusand the weight of the links between concepts, keywords,and images. The results from each strategy are limited andthen aggregated so that the final view has a representativesample from each strategy. The remainder of the processis the same as described for Fig. 4. The keywords are as-signed a different color to indicate the strategy that theyresult from, as shown in Fig. 10.

Fig. 10. Representing a sample of concepts most closely related to“horses” calculated with multiple strategies. The legend on thetop right indicates that more general concepts are shown in

red, more specific concepts are shown in cyan, andsiblings are shown in magenta

3.7 Emphasis techniques

In the SFEV framework, emphasis techniques modify thevisual representation of information to reveal changes inDOI. The goal of the emphasis techniques is to align thevisual weight of objects with their importance in a par-ticular context so that the most interesting objects areimmediately apparent (i.e., “pop out”) and less interest-ing objects fade to the background. The relative contrastcreates a visual ordering that allows a user to rapidlyand opportunistically access contextual information. Al-though this approach is conceptually straightforward, inpractice there are complex interactions between the vi-sual scales used to encode interest, the distribution ofDOI values, and the density of information in the display.The most effective visual scale for presenting quanti-

tative information, such as DOI, is position [7]. For thisreason, position is almost always used by visualizationtechniques to encode the relationships between the pri-mary data values. For example, we use position in thisprototype to show global relationships between imagesand keywords using the spring layout. Graphical fisheyeviews often distort position and scale to increase the vis-ibility and detail of information spatially near the focus.Because the interest metrics used by SFEVs are indepen-dent of spatial properties, distorting position to empha-size relative interest would be too disorienting to sup-port rapid exploration. For this reason, we do not distortposition in this prototype. Instead, we use a combina-tion of size, color saturation, and density to encode DOI.These visual scales are less effective for precise quanti-tative comparisons of interest, but they are reasonablyeffective for showing categorical and ordinal information(e.g., membership in the set of interesting objects, andrelative order).We use different combinations of emphasis techniques

to create different layers of information. Background in-formation has high contrast and distortion so that onlya small representative portion of the entire collection isshown in detail. Highlighted objects have higher mini-mum and maximum values to make them more visuallyapparent than most other information.

3.8 Interaction

Responsiveness is critical in maintaining a coherent visu-alization and allowing rapid opportunistic search; there-fore, the interface uses brushing rather than selection toactivate the focus of the fisheye view. This makes the in-terface highly sensitive to changes in the focus but alsopotentially disorienting and difficult to understand if toomuch information changes at the same time. The pro-totype uses animated transitions and multiple threadsto immediately begin or immediately interrupt metricswhen the focus changes.A second critical element of SFEVs is controlling the

visual complexity of transitions and final views. We used

P. Janecek, P. Pu: An evaluation of semantic fisheye views 49

hierarchical goal decomposition to analyze the sequenceof actions and information required at different steps inthe opportunistic task [4]. We noticed that in the pro-cess of analyzing the images in the view, users lookedat information in a predictable sequence. Users wouldfirst rapidly scan over the images and captions in thecollection. When they found an image that was inter-esting, they would pause to look at the keywords andcompare them to the visual content of the image. Thehierarchical goal decomposition allowed us to identify theknowledge required at each step of this process and limitthe complexity of the view to the minimum informationneeded. Though subtle, the pause effectively separatesthe rapid course analysis of images and their captionsfrom the more detailed analysis of correlating keywordsand images.

4 An experiment: comparing similarity-guidedand semantic-guided search

Opportunistic search is an interactive process where aninformation seeker applies a variety of strategies in reac-tion to and incorporating the information they encounterwhile searching. In the context of exploring an unstruc-tured annotated image collection, a person will browseover images and keywords, analyzing and synthesizingtheir content, and then using this information to guidethem toward images that may be related in a variety ofdifferent ways. For example, in addition to looking forsimilar images, they may also look for more general, morespecific, or alternative examples of a subject.It is difficult to measure the effectiveness of an inter-

active search tool for the same reason that these tools areso essential; user needs are often complex and continu-ously change in reaction to the information encountered.An important component of interactive search is learn-ing the vocabulary and semantic structure of the domainof interest. This domain knowledge will influence a user’sability to describe what they are looking for and to rec-ognize and evaluate the usefulness of results. SFEVs aredesigned to interactively reveal information that is re-lated to the user’s continuously changing focus. In thissection we describe the motivation, design, and results ofa formal experiment comparing the effectiveness of twotypes of SFEVs for learning the domain knowledge that isessential for opportunistic search.Standard metrics for evaluating interactive search in-

terfaces are still evolving. For example, the interactivetrack at the 2003 Text REtrieval Conference (TREC)compared the effectiveness of interfaces for interactiveWeb-based “topic distillation” tasks. Users were asked tocreate a resource list of the most authoritative Web sitesfor a broad subject area. The interfaces were evaluated bycomparing the resulting resource list using metrics suchas the relevance and level of detail of each document se-lected and the coverage and repetition of the entire result

list, which are similar to the classic precision and recallmetrics for evaluating information retrieval algorithms.An alternative metric suggested by Pirolli et al. is

the incidental knowledge learned by users during thesearch process [22]. In an experimental comparison ofa similarity-based interface with the cluster-based Scat-ter/Gather interface, Pirolli et al. asked participants todraw concept maps of a document collection after theycompleted a search task. They found that the conceptmaps of participants using the Scatter/Gather interfacewere significantly broader and more detailed than thosefrom the ranked-list interface. Based on these findings,they suggested that the Scatter/Gather interface mightbe particularly useful for sensemaking in unstructuredcollections of documents where the user is unfamiliar withthe domain. Other researchers have proposed that infor-mation seekers mentally construct informal schemas aspart of their sensemaking process to translate their do-main knowledge into the vocabulary of the collection andto guide their search [25].The TREC and Scatter/Gather experiments both in-

directly evaluated the effectiveness of the interfaces fordiscovering the structure and diversity of a subject inan unstructured document collection. We initially con-sidered designing a similar “incidental knowledge” ex-periment to evaluate the SFEV interactive visualizationtechnique. The difficulty we encountered with designinga similar experiment was how to measure the discovery ofdomain knowledge. In pretests, we found that attentionto peripheral information as well as the skill of develop-ing concept maps varied greatly between participants.Weeventually decided to reduce this variability by makingthe discovery of domain knowledge the primary task. In-stead of asking participants to create a concept map aftertheir search, we provide them with a partially filled con-cept map and ask them to find images and concepts thatwill complete the map. This is a more structured versionof the TREC “topic distillation” task.Our prototype provides two alternative methods for

discovering domain knowledge in a collection of images.The first technique guides navigation by keyword simi-larity at the document (i.e., image) level, and the secondreveals semantic relationships at the keyword/conceptlevel. The second interface is designed specifically to en-able navigation over generalization and specializationconcept hierarchies, which Bates identified as a commonsearch strategy [1].The goal of our experiment is to compare the effec-

tiveness of these two interaction techniques in a complexsearch task. Our primary hypothesis is as follows:

H1: Users will be more effective in opportunistic searchand sensemaking tasks with an interface that revealssemantic relationships rather than keyword similarity.

The experiment is a within-subject design, with in-terface and image collection as the independent variables(Table 1). The two interfaces are identical except for the

50 P. Janecek, P. Pu: An evaluation of semantic fisheye views

interest metrics that the fisheye view is based on. Thefirst interface uses content-based similarity metrics to cal-culate the distance between images, and the second usesinterest metrics that calculate the distance between con-cepts. We carefully constructed two separate image col-lections of equal size and similar subject diversity.Each participant completed one complex search task

with each of the interfaces. We used two different imagecollections to avoid learning effects and counterbalancedcombinations of variables between subjects to avoid ordereffects. A summary of the pattern of combinations forthe first four participants is shown in Table 2 and was re-peated to accommodate additional participants. In thefollowing sections, we describe the design of this experi-ment in greater detail, followed by a discussion of results.

4.1 The task

The interactive task for the experiment is complex andinvolves both opportunistically searching over the col-lection and making sense of the information encoun-tered. We created two task scenarios that are identicalexcept for the topic and are based on examples fromthe TREC 2003 Interactive Track. For the topics, we se-lected two different animal hierarchies because they arerelatively easy for participants to understand, they arewell-defined in both the image collection annotations andWordNet, and they are easy to recognize visually.In each scenario, participants were told they were as-

sistants for a biology course and were asked to find im-ages for a Web site to accompany lectures on the subject“hoofed mammals” or “sea mammals.” The script for the“sea mammals” task is as follows:

We are assistants in charge of a Web site for a biol-ogy course that will be taught next semester for 16-

Table 1. Independent variables

Variable Instance Description

1. Interface S Semantic distanceC Content similarity

2. Image collection H orses “Hoofed mammals”W hales “Sea mammals”

Table 2. Within-subject experimentaldesign. Each participant completed twotasks with a different combination of in-terface type (S,C) and image collection

(H,W)

Participant Task 1 Task 2

1 S ·H C ·W2 S ·W C ·H3 C ·W S ·H4 C ·H S ·W

year-olds. The professor is going to prepare a les-son on whales and other mammals that live in theocean and would like us to find images to put onthe Web site. The professor quickly sketched the ba-sic organization of the Web site, but we need to fillin the details so he can finalize the course materialaccording to the images available.

After reading the scenario, participants were asked tocomplete a partially filled hierarchy by exploring a collec-tion of relevant images to find keywords for each node andrepresentative images for each leaf. Participants alwaysbegan their search from a single visible image represent-ing a concept at the deepest level in the hierarchy, for ex-ample “Beluga” in the “Sea mammals” taxonomy shownin Fig. 11. The taxonomy was partially filled with a repre-sentative concept from each level (shown in bold) and di-vided into regions that are progressivelymore distant fromthe starting image.Todiscover concepts thatwill completethe taxonomy, participants navigate over the relationshipsbetween images and keywords in the collection. The ac-tual concept hierarchy ismuchmore complex than the tax-onomy shown above, and the images are related in diverseways, as explained in the following section.

4.2 The image collections

We carefully selected 100 images to represent the con-cepts in the “hoofed mammals” and “sea mammals” tax-onomies. As described earlier in Sect. 3.2, most imagescontain a list of keywords that are a path to a specificconcept in a classification hierarchy. Corbis added theselists to the images by expanding concepts using their pro-prietary thesaurus. For example, an image with the key-word “killer whales” will often also include the keywords“toothed whales”, “whales”, and “sea mammals.” Thesekeywords may be mixed with many others describingother subjects in the image, but close inspection would al-low a user to recognize or learn the existence of the moregeneral concepts. Bates identified this type of navigationas the SUPER search tactic commonly used by informa-tion retrieval experts when there are too few results toa query [1].

Fig. 11. The template for the “sea mammals” scenario. Thenodes with the gray background were given, andparticipants were asked to find keywordsand images to fill the remaining nodes

P. Janecek, P. Pu: An evaluation of semantic fisheye views 51

Although in most cases the concept hierarchy in theCorbis thesaurus closely matches the WordNet is-a-kind-of hierarchy, we had to correct several image annotationswhere there were significant conflicts between the Word-Net and Corbis vocabulary and structure that would af-fect the experiment. First, we corrected differences in vo-cabulary for keywords that were required for navigatingin the hierarchy. For example, Corbis uses the term “seamammals” while WordNet uses “aquatic mammals.” Inthis case, we replaced the inexact match

ki⇒{l1, l2} , (2)

where the keyword ki = {“sea mammals”} is mapped tothe two lemmas l1 = {“sea”} and l2 = {“mammal”}, withthe exact match

ki→{l1} , (3)

where ki = {“sea mammals”} and l1 = {“aquatic mam-mal”}.Second, we corrected a structural conflict between

Corbis andWordNet for the position of the “gray whales”concept. Corbis incorrectly places “graywhales” as a subcategory of “toothed whales” when theyare actually “baleen whales.” Because of this error in theCorbis thesaurus, most images annotated with the key-word “gray whales” are also annotated with the keyword“toothed whales.” We repaired this error by replacing thelink for “toothed whales” with a link for “baleen whales”in the affected images.

4.3 The interfaces

The two interfaces were identical in appearance and gen-eral functionality. At the beginning of the experiment,only the starting image was shown in detail; the rest of theimages were represented as gray rectangles, and the key-words were not displayed. Both interfaces support spatialnavigation using panning and zooming, and structuralnavigation by brushing over images and keywords.The main difference between the two interfaces is the

interest metric that they use for browsing over the collec-tion. One interface uses content-based similarity metricsto find the ten images or keywords that are most similarto the focus. The second interface uses a combination ofmetrics based on semantic distance in WordNet and re-veals a sample of the different senses of a keyword anda limited number of related concepts.It is important to note that the hierarchical infor-

mation necessary to complete the task is accessible inboth interfaces. Many of the images are directly anno-tated with a set of keywords that corresponds to thepath from the most specific concept present in the imageup to a more general superconcept. The difference isthat the relationships between the terms in the hier-archy are only explicitly shown in the semantic-basedinterface. When a participant does not find or recog-

nize related concepts using the content-based fisheyeview, they cannot be certain of whether the annota-tions are missing or whether the relationship does notexist.Participants controlled navigation indirectly by telling

an expert what images and keywords they would like tosee, but they did not physically interact with the inter-face. We noticed during trial studies that there was a verylarge variation in how quickly different users became com-fortable controlling the interface, especially when theinterface was slow to respond or reacted in unexpectedways. The goal of the experiment was to test how the in-terface supports semantic navigation, so we decided touse an expert “driver” to avoid keystroke-level problems.This also removed another dimension of variability be-tween participants.

4.4 Experimental results

The main hypothesis of our experiment was that par-ticipants would be more effective at a complex directedsearch task with the interface that reveals semantic rela-tionships within the collection rather than the interfacethat reveals similarity. We collected a range of quantita-tive and qualitative data during the experiment from theconcept hierarchies that participants completed, their logfiles, and their questionnaires. In this section, we use theresults of the experiment to compare the effectiveness ofthe two interfaces by analyzing user performance, brows-ing behavior, and satisfaction.

4.4.1 Differences in performance

We evaluated the performance of the interfaces based onthe accuracy of the participants’ concept hierarchies andtheir confidence. We also compared the time participantstook to complete the hierarchies, although they were notunder any pressure to finish quickly.Summaries of the results comparing the interfaces for

recall, accuracy, and confidence are shown in the sets ofstrip charts and box plots in Figs. 12–14. The upper setof data in each chart is for the semantic interface (S), andthe lower set is for the content-similarity interface (C).Under each figure, we give the mean (X), median (M ),number of participants (n), and probability of signifi-cance (p). Using an ANOVA test, we found significantdifferences between the interfaces for each of these mea-sures. We did not find any significant difference betweenimage collections or the order in which participants usedthe interfaces for these results.The results from this analysis suggest that the seman-

tic interface was significantly more effective than the sim-ilarity interface for this complex search task. Participantsusing the semantic interface (S ) found significantly moreconcepts (p < 0.02, Fig. 12), significantly more of the con-cepts they found were correct (p < 0.01, Fig. 13), and theywere significantly more confident of the concepts they

52 P. Janecek, P. Pu: An evaluation of semantic fisheye views

Fig. 12. Percent of total concepts found (S: X = 0.89,M = 0.91; X = 0.73, M = 0.68; n= 16, p= 0.02)

Fig. 13. Percent of concepts found that were correct(S: X = 0.85,M = 0.91; C: X = 0.55, M = 0.45 ;

n= 16, p < 0.01)

Fig. 14. Percent confidence that concepts found werecorrect (S: X = 0.78, M = 0.78; X = 0.63,

M = 0.6; n= 16, p < 0.01)

found (p < 0.01, Fig. 14) than with the content-similarityinterface (C ).In our analysis of the time participants took to com-

plete each task by interface, the ANOVA test did findsignificant differences between image collections. A com-parison of the interfaces within each image collectionfound no statistical difference in total time for the Horsescollection but indicated that participants using the se-mantic interface took significantly longer to complete thehierarchy in the Whales collection (n= 8, p < 0.05). Fig-ure 15 shows the detailed results. This suggests that theWhales collection was more difficult to browse over usingWordNet.One likely contributor to this difficulty was a con-

flict between the general knowledge of the participantsand the formal classification in WordNet. The most ob-vious example of this occurred in the relationship be-tween dolphins and whales. In the biological classifica-tion that Corbis and WordNet share, dolphins are clas-sified as a type of toothed whale, and beluga whales,killer whales, and pilot whales are classified as sub-types of dolphin (Fig. 16). However, most of the par-ticipants were hesitant to accept this classification. The

Fig. 15. Total time to complete the task, grouped byinterface (S,C) and image collection (W,H). Thedifference between SW and CW is significant(SW: X = 24, M = 24; CW: X = 18,

M = 19; n= 8, p= 0.034)

Fig. 16. A portion of WordNet showing the relationshipbetween whales and dolphins. Almost every user wasconfused by the classification of “killer whales”

as a type of dolphin

longer time that participants took with the semanticinterface could reflect the difficulty they had in re-solving this conflict using the information they hadavailable.

4.4.2 Differences in browsing behavior

To analyze differences in browsing behavior, we extractedthe number of distinct keywords and images visited byeach participant as they were searching for concepts, aswell as the rate at which they discovered new information.We found that participants using the semantic interfacebrowsed to nearly twice as many distinct keywords asthey did with the similarity interface but only slightlyfewer images (Fig. 17). Figure 18 shows the number ofdistinct images and keywords browsed for each concept

P. Janecek, P. Pu: An evaluation of semantic fisheye views 53

found. Both figures indicate that participants browse pri-marily with images in the content similarity interface andprimarily with keywords in the semantic interface.There are two likely reasons for this difference. First,

each interface encourages the user to focus on differenttypes of information in a collection. The semantic in-terface emphasizes the hierarchical relationships betweenconcepts, so users will naturally focus on the keywords.On the other hand, the content-similarity interface em-phasizes images that are similarly annotated, allowingusers to navigate directly between images without select-ing a keyword. Users select a keyword only when theywant to limit their search to a particular feature. Sec-ond, the interfaces show the same number of images, butnot the same number of keywords. The semantic interfacemay show 10 keywords in each of the SUPER, SUB, andSIBLING hierarchies, while the similarity interface showsonly the ten most important keywords for an image. Par-ticipants will therefore see and have the opportunity tovisit many more keywords with the semantic interface.Close inspection of the keyword data for the se-

mantic interface in Fig. 18 (the hollow circles labeled

Fig. 17. Distinct keywords and images browsed with each inter-face. Participants browsed significantly more keywords with the se-mantic interface (C.kwd: X = 10,M = 10; S.kwd: X = 20,M = 19;n = 16, p < 0.001), and significantly more images with the con-tent similarity interface (C.img: X = 20, M = 19; S.img: X = 16,

M = 15; n= 16, p= 0.015)

Fig. 18. Average number of distinct images and keywordsbrowsed per concept found with each interface

K_Semantic) reveals three distinct groups that roughlycorrespond to the different regions in the task concepthierarchy (e.g., four concepts in the first region). Thesesteps could correspond to the difference between search-ing for subconcepts and searching for siblings as usersmove progressively further from the start image. Wewould need to conduct additional experiments to furthercharacterize these differences.

4.4.3 Questionnaires

The exit questionnaires asked participants to comparethe interfaces along a number of dimensions using a 7-point scale and to give comments about what they likedand disliked about the systems. The questions compar-ing the two interfaces followed the pattern of “Whichsystem is better for . . . ”. Participants responsed usinga 7-point scale (1 = Content–Similarity, 4 = Neither ,7 = Semantic). In the actual questionnaires, the pos-ition of the interfaces at the ends of the scales changedto reflect the order in which the participant used theinterfaces.The first question asked participants how different

they found the two interfaces. Visually, the two interfacesare nearly identical. However, during interaction the sim-ilarity and semantic-interest metrics reveal very differenttypes of information. Participants found the interfaces tobe very different (Median = 6).Questions 3 and 4 asked which interface was “eas-

ier to learn to use” and “easier to use.” We expectedparticipants to favor the content-similarity interface forease of use because the semantic interface is visually andcognitively more complex: it presents more keywords atone time and superimposes multiple types of relationshipsthat require a legend to decode. However, participantsrated the semantic interface easier in both cases (Q3:M = 6; Q4:M = 6). One interpretation of these results isthat the semantic interface is easier for the type of searchthat participants were asked to perform. This interpreta-tion is also implied by several comments that identify gen-eral “ease of use” as a strength of the similarity interface:

(P1): “Easier for a beginner. You don’t have to think,just point and click.”

(P14): “Less complicated. Simple.”(P16): “Simple, accessible, instantly understood relation-

ships.”

The remaining questions compared the effectivenessof the interfaces for the task scenario. Using an ANOVAtest, we found a significant difference in responses forseveral questions based on which interface the partic-ipants used first. Table 3 we summarize the responsesby interface order, and Fig. 19 shows an overview ofthe distributions. The results suggest that participantsthat used the semantic interface first tended to preferit more strongly for learning about the topic (Q2, p <0.05), finding different kinds of images (Q6, p < 0.01),

54 P. Janecek, P. Pu: An evaluation of semantic fisheye views

Table 3. Median response from exit questionnaire comparinginterfaces, separated by interface order

Question CS SC p

Q2. Learning about topic? 6 7 p < .05Q5. Finding same kind of image? 6 5Q6. Finding different kinds of images? 5 7 p < .01Q7. See more images? 5 7 p < .05

Fig. 19. Responses to questions 2, 5, 6, and 7 of exitquestionnaire, grouped by the order participants used the

interfaces

and seeing more of the images in the collection (Q7,p < 0.05). One possible explanation for this order effect isthat participants develop a strategy for finding informa-tion using the first interface and then attempt to continueusing the same strategy in the second interface. Tran-sitioning from the guided exploration possible with thesemantic interface to the unstructured exploration of thesimilarity interface is apparently more frustrating thanvice versa.Participants strongly preferred the semantic interface

for finding both similar (Q5) and different kinds of im-ages (Q6). We expected that the semantic interface wouldbe more effective for finding different images because itis specifically designed to provide a sample of the diver-sity of related concepts. However, we did not expect thatthe semantic interface would be more effective for findingsimilar images than the similarity interface. A possible in-terpretation of this result is that participants understood“the same kind of image” to mean within a single conceptrather than a combination of features at the document orimage level. However, their comments about the similar-ity interface suggest that they were often frustrated byexactly this document-level type of similarity:

(P9): “Images were too similar.”(P8): “It felt like I was turning in circles [of the same

type of image].”(P1): “Difficult to get out of an image topic. For ex-

ample, it was difficult to get out of Orca because Iwas stuck in Dolphin.”

(P6): “Must find the most uncommon feature of animage to find other images.”

Several participants suggested that they would like tobe able to refine and control the similarity metric:

(P16): “Search is often an intersection of concepts, so Iwant to be able to select more than one.”

Participants also preferred the semantic interface forlearning about the topic structure (Q2) and exploringmore of the images in the collection (Q7). Participantscommented that the semantic interface was easier for dis-covering and verifying their understanding of the concep-tual relationships between keywords and better for dir-ected navigation.We were surprised to find that the additional complex-

ity of the semantic interface was not viewed as a prob-lem, and that, although the interface required more con-centration to use, the participants generally appreciatedthe additional information. On the other hand, opinionswere mixed about the complexity of the similarity in-terface: some participants suggested that the interfaceshould show more images, and others commented thatthere were already too many. Increasing the user’s controlof the complexity is an important area for future researchwith this type of interaction technique.Users unanimously appreciated the hierarchical struc-

ture visible in the semantic interface. They found thismade it easier to discover and verify their understandingof the conceptual relationships between the keywords andwas useful for structuring their navigation. Several par-ticipants said the semantic interface was instructive andwould be useful as a tool for learning about a collection.The most frequent weakness cited of the semantic

interface was that it was difficult to perceive distancewith saturation. Several participants also suggested thatthey would like to have automated commands to navigatealong hierarchies and through history (e.g., upwards andbackwards) and that the interface should show the rela-tionships between objects more clearly. Developing moreeffective visual cues and interaction for SFEV techniquesis an important area of future research.

4.5 Discussion of results

The results of the experiment showed a strong advan-tage of the semantic-guided interface over the similarity-guided interface for opportunistic search and sensemak-ing tasks. Sensemaking, in particular, requires learningconceptual relationships such as the generalization andcomposition hierarchies shown by the semantic interface.Participants found significantly more concepts, madefewer errors, and hadmore confidence in their results withthe semantic interface. The weaker performance of userswith the similarity-guided interface suggests that mak-ing sense of the relationships between keywords withoutany visual cues is a difficult task. Furthermore, we foundsignificant differences in browsing behavior between in-terfaces.With the semantic interface participants focusedon the keywords to explore the collection, whereas thecontent-similarity interface allowed participants to focusprimarily on images.

P. Janecek, P. Pu: An evaluation of semantic fisheye views 55

5 Conclusion

Opportunistic search and sensemaking in large infor-mation collections are highly interactive tasks that arepoorly supported in current interfaces. These types ofsearch activities require rapidly discovering, analyzing,and navigating between the relationships within informa-tion collections. A significant obstacle for users to effec-tively search over unstructured collections is their lackof domain knowledge, such as the vocabulary and se-mantic structure of the collection. We propose seman-tic fisheye views as an interactive visualization techniqueto support effective exploration over unstructured collec-tions of information by guiding navigation using similar-ity and semantic relationships. Fisheye views reduce thevisual complexity of displays by selectively emphasizingthe most important information in a representation anddeemphasizing or filtering less important information.We have conducted an experiment comparing the

effectiveness of similarity- and semantic-based fisheyeviews for complex search tasks. The results of this ex-perimental evaluation suggests that the semantic-guidedinterface is significantly more effective for complex sense-making tasks than the similarity-guided interface. Theseresults strongly support semantic fisheye views as an ef-fective interactive technique for opportunistic search andsensemaking tasks. An important implication of theseresults is that similarity-based search, which is basedon algorithms used by common query and cluster tech-niques, is significantly less effective than semantic-guidedsearch when semantic diversity is important.The main contribution of this research is a characteri-

zation of some of the strengths and weaknesses of seman-tic fisheye views and of results that suggest that these arepromising techniques for opportunistic search and com-plex sensemaking tasks.

Acknowledgements. This paper is based in large part on un-published research in [12]. This research was funded by Grant2000-066816 from the Swiss National Science Foundation and wassupported by the generous permission of Corbis Corporation to di-rectly access an electronic version of their annotated images. Wealso thank the helpful suggestions of the anonymous reviewers.

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