Assessing Subject Access for Images

HANNAH MARSHALL

METADATA LIBRARIAN FOR IMAGE COLLECTIONS

MWG MAY 20, 2016

Outline Background Research questions Research design Findings Applications Conclusions

The Arts & Sciences Images for Teaching Collection

Challenges of Subject Analysis for Images "Image indexing is a complex socio-cognitive process that involves processing sensory input through classifying, abstracting, and mapping sensory data into concepts and entities often expressed through socially-defined and culturally-justified linguistic labels and identifiers" (Heidorn, 1999)

"Concept-based indexing has the advantage of providing higher-level analysis of the image content but is expensive to implement and suffers from a lack of inter-indexer consistency due to the subjective nature of image interpretation" (Chen, Rasmussen, 1999)

Assessment Goals• Determine retrieval rates

• Approximately how successful are subject searches for images in our teaching collection?

• Analyze search utility• Is subject metadata a good access point for images?

• Test a framework for training image catalogers and structuring visual literacy outreach

• Is there an easy way to improve the search utility and retrieval rates for these images?

Study DesignData Sets

Existing Existing images and metadata from the

Teaching Collection Metadata created by many different image

catalogers from varying backgrounds over a long period of time

Gathered Survey to gather subject terms from

participants for each image (for comparison against subject terms in existing metadata set)

Participants = undergraduate A&S students currently enrolled in an Art History or Classics course

VS.

Study DesignVariable Group

Control version Variable version

Research Questions

1. What is the level of correspondence between the existing subject terms for these images and the participant-assigned terms?

2. What is the level of correspondence in the types of subject terms assigned by participants and those in the existing metadata?

3. Does providing users with a framework for analyzing the subject of an image change the nature and content of the subject terms they choose to assign to the image?

Research Question #1

What is the level of correspondence between the existing subject terms for these images and the participant-assigned terms?

TRANSLATION: Do users search for images using the same terms we use to describe them?

Existing Metadata: Still lifes ; Fruit ; Vessels ; Clocks ; Roemers ; Lemons ; Oysters ; Goblets

Participant responses:

Research Question # 1 Example

Ethiopian Alphabet and NumeralsWosene Worke Kosrofmid-20th century

Existing Metadata:

Participant responses:

Research Question # 2

What is the level of correspondence in the types of subject terms assigned by users and those in the existing metadata?

TRANSLATION: Do users search for images using the same TYPES of terms that we use to describe them?

Primary Terms Secondary Terms Tertiary Terms

Objects and elements are identified and named

“What is the image of?”

Objects and elements are interpreted: characters are identified, facial expressions are interpreted, gestures are ascribed meaning

“What is the image about?”

An awareness of the work/image as an expressive cultural output that is the product of a time, place, and culture

“What is the image a good example of?”

Research Question # 2Example

Primary Terms Secondary Terms Tertiary Terms

“What is the image of?”

“What is the image about?”

“What is the image a good example of?”

Women; candles; severed head; maidservant; interior; drapes; swords; murder

Judith; Holofernes Tenebrism; chiaroscuro

Judith and Maidservant with the Head of HolofernesArtemesia Gentileschi

Ca. 1625The Detroit Institute of Arts

Research Question #3

Does providing users with a framework for analyzing the subject of an image change the nature and content of the subject terms they choose to assign to an image?

TRANSLATION: Does it make a difference if we ask them these three questions about each image?

1. “What is the image of?”2. “What is the image about?”3. “What is the image a good example

of?”

Findings!

Findings - types of termsSearch Utility

Ex i st i n g Dat a User s

64%

34%

12%

13%

19%

16%

5%

37%

Types of terms

Primary Terms Secondary TermsTertiary Terms Non-Subject Terms

Primary TermsObjects and elements are identified and named“What is the image of?”

Secondary TermsObjects and elements are interpreted: characters are identified, facial expressions are interpreted, gestures are ascribed meaning“What is the image about?”

Tertiary TermsAn awareness of the work/image as an expressive cultural output that is the product of a time, place, and culture“What is the image a good example of?”

Findings - types of termsSearch Utility

Ex i st i n g Dat a

User s Ex i st in g Dat a

Use r s

71.70%

45.30%

0.00%

47.20%

26.40%

15%

16%

0%

5%

8%

13%

19%

0%

32%

17%

0.00%19.70%

0.00%

15.80%

48.60%

2d works vs. 3d works

Primary Terms Secondary TermsTertiary Terms Non-Subject Terms

2D works 3D works

Higher levels of correspondence for images of 2D works

Users were 2.5 times more likely to use non-subject terms when describing images of 3D works

Findings- types of termsSearch Utility Non-subject terms tended to capture other key descriptive access points: Culture, Materials/Techniques, Style/Period, Worktype

Worktype

Style/Period

Materials/Techniques

Culture

0% 10% 20% 30% 40% 50% 60%

Most common types of non-subject access points

Findings - literal termsRetrieval Rates Literal matches = successful image retrieval

Non-matches = unsuccessful image retrieval

Successful retrieval = 8.5% Unsuccessful retrieval = 91.5%

92%

9%

Correspondence between ex-isting metadata and users’

search terms

Non-matches Literal Matches

Findings Retrieval Rates Of that 8.5%...

• Primary Terms (75%)

• Secondary Terms (3%)

• Tertiary Terms (16%)

• Non-subject Terms (6%) - Other descriptive metadata that does not address subject meaning (i.e. materials and techniques)

Corresponding literal terms broken down by type

Primary Terms Secondary TermsTertiary Terms Non-Subject Terms

Conclusions Primary terms yield the greatest search utility and higher levels of successful image retrieval.

◦ Application: Focus image cataloging on assigning primary terms to images

High numbers of non-subject terms applied to images of 3D and non-representational works suggest that subject metadata is a weak access point for them◦ Application: Forego full subject cataloging for these works and focus on non-

subject descriptive access points