Multimedia Data Navigation and the Semantic Web (SemTech 2006)

Navigation for the Digital Universe

Multimedia Data Navigation and the Semantic Web

Valery A. Petrushin and Bradley P. Allen

Copyright © 2005 Accenture, LLP / Siderean Software, Inc. All rights reserved. 2

Outline• About the authors• Faceted Navigation• Semantic Web Techniques

– RDF(S)

– Dublin Core

– SKOS

– TGM

– LSCOM, SMIL & MPEG-7• Case Study: BBC Rushes• Implementation

– BBC Rushes Navigator• Metadata representation

• Architecture

• User interface

• Future work• Contact Information• Demo


About the Authors• Valery A. Petrushin, Ph.D.

– Sr. Researcher, Accenture Technology Labs

– Semantics of programming languages

– Multimedia data mining, analysis, annotation and retrieval

– Georgia Tech, Glushkov Institute for Cybernetics

• Bradley P. Allen– Founder and CTO Siderean Software, Inc.

– Semantic-based navigation, Web personalization services, case-based reasoning

– Former founder and CTO of Limbex Corp. and TriVida Corp.

– Carnegie-Mellon University


Faceted navigation• Facets are metadata properties whose ranges

form a near-orthogonal set of controlled vocabularies

• Creator: “Dickens, Charles”• Subject: Arsenic, Antimony• Location: World > U.S. > California > Venice

• Facets form a frame of reference for information overview, access and discovery

• Other properties serve as landmarks and cues

• Faceted navigation uses facets to provide end user access and discovery in the context of large collections of semi-structured information


Faceted Navigation BuiltUsing Semantic Web Standards

• Define/reuse ontologies expressed in RDF(S)/OWL

• Classes for defining instances and controlled vocabularies• Properties for facets and additional asset metadata attributes

• Import/transform aggregated instance metadata into an RDF representation

• Resources referred to via URIs• Content and controlled vocabularies

• Write application profiles in terms of RDF


Building Faceted Navigation Applications

… then represented as instances of concepts in

ontologies and tagged using controlled vocabularies…

… then application profilesare created…

… that define navigation services for user applications

Metadata is aggregated…

Term

Event

Person

PlaceText

Application Profiles


Semantic Web Technology

• RDF(S) – Resource Description Framework (Schema) • Dublin Core• SKOS – Simple Knowledge Organization System• TGM-I & II – Thesaurus for Graphic Materials • LSCOM – Large Scale Concept Ontology for Multimedia• SMIL – Synchronized Multimedia Integration Language• MPEG-7 – Multimedia Content Description Interface


RDF (S)• RDF (S) - Resource Description Framework (Schema)

• http://www.w3.org/RDF/• http://www.w3.org/TR/rdf-schema/• language for representing metadata about Web resources• Triple : subject – predicate -- > object • Example:

<?xml version="1.0"?>

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:contact="http://www.w3.org/2000/10/swap/pim/contact#">

<contact:Person rdf:about="http://www.accenture.com/techlabs/VAP/contact#me">

<contact:fullName>Valery A. Petrushin</contact:fullName>

<contact:mailbox rdf:resource="mailto:[email protected]"/>

</contact:Person>

</rdf:RDF>

http://www.w3.org/RDF/


Dublin Core (DC)• Dublin Core

• http://dublincore.org/documents/ • vocabulary for describing documents (title, creator, subject,

description, publisher, contributor, date, type, format, identifier, source, language, relation, coverage, rights)

• Example:

<?xml version="1.0"?>

<!DOCTYPE rdf:RDF PUBLIC "-//DUBLIN CORE//DCMES DTD 2002/07/31//EN"

"http://dublincore.org/documents/2002/07/31/dcmes-xml/dcmes-xml-dtd.dtd">

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:dc="http://purl.org/dc/elements/1.1/">

<rdf:Description rdf:about="http://www.accenture/techlabs/Petrushin">

<dc:title> Multimedia Data Mining and Knowledge Discovery</dc:title>

<dc:creator> Valery A. Petrushin </dc:creator >

<dc:publisher>Springer Verlag</dc:publisher>

</rdf:Description>

</rdf:RDF>


SKOS• SKOS – Simple Knowledge Organization System

• http://www.w3.org/2004/02/skos/ • model for expressing structure and content of concept schemes

(thesauri, taxonomies, etc.)• Specifies concepts, collections of concepts and relations between

concepts (broader, narrower, related)• Example:

<rdf:RDF

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:skos="http://www.w3.org/2004/02/skos/core#">

<rdf:Description rdf:about="http://www.example.com/concepts#people">

<skos:broader rdf:resource="http://www.example.com/concepts#mammals"/>

<skos:narrower rdf:resource="http://www.example.com/concepts#children"/>

<skos:narrower rdf:resource="http://www.example.com/concepts#adults"/>

</rdf:Description>

</rdf:RDF>


TGM – I & II• TGM – Thesaurus for Graphic Materials (The

Library of Congress)• TGM-I – Subject Terms (6,300)

– http://www.loc.gov/rr/print/tgm1/toc.html

• TGM-II – Genre and Physical Characteristic Headings (600)– http://www.loc.gov/rr/print/tgm2/

• Example:

TGM-I:

Term: Sand

Narrower Term: Quicksand

Related Term: Dunes, Sand sculpture, Sandpaintings

TGM-II:

Term: Aerial views

Public Note: Views from a high vantage point.

Used For: Air views, Balloon views, Views, Aerial

Broader Term: Views

Narrower Term: Aerial photographs

Related Term: Bird's-eye views, Panoramic views


LSCOM, SMIL & MPEG-7• LSCOM – Large Scale Concept Ontology for

Multimedia• http://www.acemedia.org/aceMedia/files/multimedia_ontology/

presentations_1st_meeting/arda.pdf

• SMIL – Synchronized Multimedia Integration Language

• http://www.w3.org/TR/REC-smil/ • Simple language for representing multiple synchronized media

streams

• MPEG-7 – Multimedia Content Description Interface

• http://www.chiariglione.org/mpeg/standards/mpeg-7/mpeg-7.htm • Advanced language for representing multimedia content• ISO Standard


Case Study: BBC Rushes• Rushes are raw footage …

with a promise to turn into golden nuggets of stockshots

• TRECVID 2005• Video Retrieval Competition at NIST• http://www-nlpir.nist.gov/projects/trecvid/

• Problem:• create a system that helps a TV program maker

compose a video using current clips and rushes

• Data Statistics:– Duration: 49.3 hours– Content:

– Clips about vacation and travel– 4 issues of “Summer Holiday” (~ 2 hours)– BBC One News (30’) + fragment (~3’)


BBC Rushes: Data Statistics - 1• Statistics: clip level

• 615 clips (308 development + 307 test sets)

• Duration (mm:ss) :– Minimal / Maximal - 00:03.48 / 47:11

– Mean / Median – 04:49 / 02:25

– Std - 06:02.73

• Keywords:– Different keywords / Occurrences –

1036 / 4908

– Mean / Median – 7.98 / 7

– Minimal / Maximal – 0 / 34


BBC Rushes: Data Statistics - 2• Statistics: shot level

• Number of shots 10,064• Shot duration (mm:ss)

» Minimal - 0:00.04» Maximal –

22:45.16» Mean – 0:17.51» Median – 0:09.74» Std -

0:33.97

• Number of key frames» Total: 39,132» Median per shot: 2» Mean per shot: 3.8» Maximal: 377» Minimal: 1


BBC Rushes: representation• Ontologies

– RDFS, Dublin Core, SKOS• Controlled vocabularies

– TGM-1 (reflecting Light Scale Concept Ontology for Multimedia), ISO8601 (temporal hierarchy of dates), MPEG-7 (visual features)

• Instances– trecvid:Shot, trecvid:Clip

• Application profile– Retrieve instances of type trecvid:Clip

• Textual facets: dc:title (clip title), dc:subject (keywords), dc:creator (director), dcterms:created (production date), dcterms:issued (show date), dc:extent (duration)

– Retrieve instances of type trecvid:Shot• Visual facets: dc:subject with values skos:narrower than trecvid:color,

trecvid:texture and trecvid:colorplustexture

• Textual facets through reference to containing clip


Ontology Schema

Clip

Shot

KeyFrame

Color

Texture

Color+Texture

Title

Creator

Subject

Date

dcterms: partOf

dc: title

dc: creator

dc: subject

dc: subject

dc: created

skos: broader

skos: broader

skos: broader

skos: broaderskos: broader

skos: broader

skos: broader

skos: broader

VISUAL

TEXTUAL


BBC Rushes: visual facets• Facets: color, texture, [shape] +

combinations• Color, texture, color+texture

• To build facets• Extract features (MPEG-7):

– Color: dominantColor(24), colorStructure (256), colorLayout (12)

– Texture: edgeHistogram (80), homogenousTexture (60)

• SOM Clustering of keyframes– Select as a visual “word” the closest

keyframe to node centroid• Represent keyframes as SKOS concepts,

centroids as skos:broader of cluster members

• Example: – SOM for color 35x28 (=980 nodes)


Self-organizing Maps• SOM = Kohonen NN = Topology-preserving map• Unsupervised learning (Clustering + Visualization)• X = {xi} , xi Rd - input data• M = {mk} , mk Rd - prototype vectors (codebook) =

neurons on 1D or 2D grid• Training:

• 1. Start with random mk

• 2. For xi find best-matching unit (BMU) mc

• 3. Update prototype vectors in neighborhood

where is the neighborhood kernel is radius at time t

• Two phases: rough and fine tuning

kk

c mxmx min

)()()()()()1( tmtxthttmtm ickkk

tkccktckck rrddth ,),2/exp()( 22 )(thck


BBC Rushes: RDF subgraph

Chilli_peppers

v159_001.wmv v159.mpg

“michelle jones”

2000-03-01

dc:subject

dc:creator

dcterms:partOf

dc:created

dc:subject

color#26547

f000000000.jpg

skos:broader

skos:broader

2000

2000-03

Hot_peppers

PeppersYear

skos:broader

skos:broader

skos:broader

skos:broader

“thailand, chiang mai/chillis”

dc:title

Color

skos:broader


BBC Rushes: RDF/XML serialization <trecvid:Clip rdf:about="http://swvideo.techlabs.accenture.com/v159.mpg"> <rdf:type rdf:resource="&dctype;MovingImage" /> <dc:title>thailand, chiang mai/chillis</dc:title> <dcterms:extent>202200</dcterms:extent> <dc:creator>michelle jones</dc:creator> <dc:identifier>mrs320354</dc:identifier> <dcterms:created rdf:resource="tag:siderean.com,1752-09-14:2000-03-01" /> <dcterms:issued rdf:resource="tag:siderean.com,1752-09-14:2000-07-18" /> <dc:subject rdf:resource="&trecvid;thailand" /> <dc:subject rdf:resource="&trecvid;chiang_mai" /> <dc:subject rdf:resource="&trecvid;chillis" /> <dc:subject rdf:resource="&trecvid;peppers" /> <dc:subject rdf:resource="&trecvid;chilli_peppers" /> <dc:subject rdf:resource="&trecvid;vegetables" /> <dc:subject rdf:resource="&trecvid;markets" /> <dc:subject rdf:resource="&trecvid;street_markets" /> <dc:subject rdf:resource="&trecvid;food_markets" /> <dc:subject rdf:resource="&trecvid;food" /> <dc:subject rdf:resource="&trecvid;herbs" /> <dc:relation>http://swvideo.techlabs.accenture.com/v159.fset/f000000000.jpg </dc:relation> </trecvid:Clip>

<skos:Concept rdf:about="&trecvid;chilli_peppers"> <skos:broader rdf:resource="&tgm1;Hot_peppers"/> <skos:prefLabel>chilli peppers</skos:prefLabel> </skos:Concept>

<skos:Concept rdf:about='tag:siderean.com,1752-09-14:2000-03-01'> <skos:prefLabel>2000-03-01</skos:prefLabel> <skos:broader rdf:resource='tag:siderean.com,1752-09-14:2000-03'/> </skos:Concept>

<trecvid:Shot rdf:about="http://swvideo.techlabs.accenture.com/shotsWMV/v159_001.wmv"> <rdf:type rdf:resource="&dctype;MovingImage" /> <dcterms:isPartOf rdf:resource="http://swvideo.techlabs.accenture.com/v159.mpg" /> <dcterms:extent>21000</dcterms:extent> <dc:relation>http://swvideo.techlabs.accenture.com/v159.fset/f000000000.jpg</dc:relation> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000000000.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000000240.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000000280.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000001440.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000003120.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000005440.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000009680.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000011520.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000012040.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000013800.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000014800.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000015120.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000016760.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000018280.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000019360.jpg"/> <dc:subject rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000021000.jpg"/> </trecvid:Shot>

<skos:Concept rdf:about="http://swvideo.techlabs.accenture.com/v159.fset/f000000000.jpg"> <skos:broader rdf:resource="http://swvideo.techlabs.accenture.com/color#26547" /> <skos:prefSymbol rdf:resource="http://swvideo.techlabs.accenture.com/v159.fset/f000000000.jpg" /> </skos:Concept>

<skos:Concept rdf:about="http://swvideo.techlabs.accenture.com/color#26547"> <skos:broader rdf:resource="&trecvid;color" /> <skos:prefSymbol rdf:resource="http://swvideo.techlabs.accenture.com/v289.fset/f000048880.jpg" /> </skos:Concept>


BBC Rushes Navigator: Architecture

AJAX client in FirefoxMetadataAggregator

MetadataStore

NavigationWeb Services

XRBRquery

XRBRresponse

BBC Rushes RDF

http://www.siderean.com/bbcrush/bbcrush.jsp (with Firefox 1.5)


Lessons Learned• Data preparation

• Robust shot boundary detection• Careful selection of keyframes

– Motion based– Salient object based– Filtering redundant keyframes

• Using group-of-frames (GOF) features

• Concept recognition/propagation• Propagate keywords from clip to shots• Recognize concepts from visual data• Probabilistic reasoning• Derive concepts from data (data mining) + labeling


Summary• Methodology of Multimedia Data

Representation– Semantic Web Technology– Multimedia Data Mining

• Prototype of Multimedia Retrieval System– BBC Rushes– Web-based Interface using AJAX


Future work• More facets

• Shape + combinations• Geographical location

• More Interfaces• Map of the world for browsing

places• Hierarchy of SOM for browsing clips

and shots

• More Tools• Tagging tool for creating and

managing metadata• Tools for creating video databases

(shot extraction, feature extraction, clustering, classification of events, etc.)

• Tools for creating audio-video compositions (TV programs, commercials, etc.)


BBC Rushes Navigator:Navigation with LSCOM


BBC Rushes Navigator:Hierarchical Drill-down on People Facet


BBC Rushes Navigator:Faceted View of All Shots


BBC Rushes Navigator:Searching by Subject


BBC Rushes Navigator: Searching by Color, Playlist composition


BBC Rushes Navigator: Drill-down using Subject and Color


Contact Information

• Valery A. [email protected]

• Bradley P. [email protected]

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