Multimedia Signal Processing & Content-Based Image Retrieval

Multimedia Signal Processing & Content-Based Image Retrieval

Anastasios N. VenetsanopoulosUniversity of Toronto

Contact: [email protected]

http://www.dsp.toronto.eduhttp://www.ece.toronto.edu

OUTLINE

INTRODUCTION MULTIMEDIA APPLICATIONS IMPACT OF MULTIMEDIA CONTENT-BASED IMAGE RETRIEVAL

(CBIR) MPEG-7 RESEARCH ISSUES

INTRODUCTION-1

WHAT IS MULTIMEDIA? WHAT IS MULTIMEDIA PROCESSING? GOALS OF MULTIMEDIA PROCESSING

INTRODUCTION-2

DIFFICULT TO DEFINE GENERALLY CONSISTS OF:

MULTIMEDIA DATA INTERACTION SET

MULTIMEDIA DATA:MULTI-SOURCE, MULTI-TYPE, MULTI-FORMAT

INTERACTION SET:WITHOUT INTERACTIONS BETWEEN

MULTIMEDIA COMPONENTS, MULTIMEDIA IS MERELY A COLLECTION OF DATA

WHAT IS MULTIMEDIA?

INTRODUCTION-3

REAL OBJECTS

VIRTUAL OBJECTSVIRTUAL OBJECTSREAL SPEECH

MutimediaData

Components

COMPLEX INTERACTIONSBETWEEN COMPONENTS INTHE SCENE MAKE VIRTUALVIRTUALCOMPONENTS SEEM MORE REALISTIC

EXAMPLE: AUGMENTED REALITY CONFERENCE

INTRODUCTION-4

MULTIMEDIA PROCESSINGAPPLY SIGNAL PROCESSING TOOLS TO

MULTIMEDIA DATA TO ENABLE: REPRESENTATION INTERPRETATION ENCODING DECODING

WHAT IS MULTIMEDIA PROCESSING?

INTRODUCTION-5

EFFECTIVE & EFFICIENTACCESSMANIPULATIONEXCHANGESTORAGE

OF MULTIMEDIA CONTENT

GOALS OF MULTIMEDIA PROCESSING

CONTINUING…



MULTIMEDIA APPLICATIONS-1

GPS NAVIGATION

SCALABLE VIDEO

STREAMING


E-COMMERCE

TELEPRESENCE CELLULAR


MORE SPECIFIC EXAMPLES

MULTIMEDIA APPLICATION GOALS IMPROVE INTERPERSONAL COMMUNICATIONPROMOTE UNDERSTANDING OF IDEASALLOW INTERACTIVITY WITH MEDIA INCREASE ACCESSIBILITY TO DATA

MPEG-4, 7, 21 JPEG-2000 MP3 & PERCEPTUAL

CODING

MULTIMEDIA STORAGE VIDEO-ON-DEMAND DIGITAL CINEMA AUTHENTICATION

GOING ON…



IMPACT OF MULTIMEDIA-4

WORLD INTERNET USAGE (July 23, 2005)

COUNTRY CURRENT

USERS% WORLD

USERSGROWTH

(2000-2005)PENETRATION

North America 223,392,807 23.8% 106.7% 68.0%

Europe 269,036,096 28.7% 161.0% 36.8%

Asia 323,756,956 34.5% 183.2% 8.9%

Middle East 21,770,700 2.3% 311.9% 8.3%

Africa 16,174,600 1.7% 258.3% 1.8%

Latin America & Caribbean

68,130,804 7.3% 277.1% 12.5%

Oceania & Australia

33,443,448 1.8% 115.9% 49.2%

WORLD 938,810,929 100% 160.0% 14.6%


USERS (S0CIETY) DEMAND INCREASED MOBILITYEASE-OF-USEPERSONAL CUSTOMIZATIONDEVICE FLEXIBILITYHIGH LEVEL OF COLLABORATION WITH PEERS

DEVICES MUTATE AND BECOMEMULTI-FUNCTIONAL, NOT SPECIALIZEDEFFORTLESSLY PORTABLE, NOT STATIONARYUBIQUITOUSLY NETWORKED, NOT ISOLATED

MULTI-FUNCTIONAL DEVICES MUSTBROWSE INTERNETENTERTAINBE EASY-TO-USE

CUSTOMIZATIONPERSONALIZATION (THEMES, PREFERENCES)

NETWORKEDCAPABLE OF CONNECTING TO MANY

DIFFERENT NETWORKS (INTERNET, P.O.T.S., LAN, CELLULAR, BLUETOOTH, 802.11b, GPS)

FACILITATE MANY TYPES OF WORKFLOW MANAGE USER’S TIME


CONVERGENCE

TECHNOLOGIES WHICH WERETOTALLY UNRELATED 10 YEARSAGO ARE NOW UNIFIED UNDERTHE CONCEPT OF MULTIMEDIA


EXAMPLE: CELLULAR PHONES


PRIMARY CONSUMER USE: WIRELESS TELEPHONY

CONVERGED USES PERSONAL ORGANIZER INTERNET BROWSER/EMAIL ENTERTAINMENT (MP3, RADIO)

VIDEO/STILL CAMERA PAGER/MESSAGING (SMS)


DEMANDS FUNCTIONALITYCONSUMPTION OF MANY MEDIA TYPESCONNECTIVITYPORTABILITY, ETC.

RESULTHIGHLY COMPLEX DEVICESPUSH TOWARDS DENSE CIRCUITRYMULTIMEDIA DEVICES BECOME UBIQUITOUSDEVICES GENERATE MULTIMEDIA DATA

(INCLUDING IMAGES, VIDEO, AUDIO)

OVERALL

MOVING ALONG…



MOTIVATION & GOALS WHAT IS CBIR? CONTRIBUTING DISCIPLINES APPLICATION SCENARIOS SOME SPECIFIC ISSUES TYPICAL CAPABILITIES

CBIROVERVIEW

EFFECTS & PROCESSING

RESULT: DIGITAL MEDIA FLOODHOW DO WE COPE, TRACK, ORGANIZE IT ALL?

POLAROID FILED FOR BANKRUPTCYHAS DIGITAL KILLED FILM? IF SO, WHY?

CHEAP & DENSE STORAGE

CBIRMEDIA FLOODING

EXAMPLE: GENERAL PHOTOGRAPHY

SNAPSHOT PREVIEWS

EASY SHARING VIA INTERNET

MEMORY REUSABLE

PRINTER TECHNOLOGY

DEVICE FUNCTION CONVERGENCEDATA RAPIDLY GENERATED BY MANY DEVICES INTERNET ACTS AS GLOBAL TRANSPORTDATA CONSUMED BY DEVICES ON DEMAND

MULTIMEDIA DATA NEEDS TO BEEFFICIENTLY STORED INDEXED ACCURATELYEASILY RETRIEVED

CBIRMOTIVATION

CONTENT BASED IMAGE RETRIEVAL PART OF MULTIMEDIA INDEXING

IMAGES (2-D SPACE-DEPENDENT SIGNALS)VIDEO (TIME-VARYING IMAGE SET)AUDIO (1-D TIME-DEPENDENT SIGNALS)TEXT (e.g. BOOK INDEX, SEARCH ENGINES)

COMPUTER BASED HIGHLY AUTOMATED DIFFICULT TO DO PROPERLY

CBIRIS…

FOR A GIVEN QUERY…EXAMPLE IMAGEROUGH SKETCHEXPLICIT DESCRIPTION CRITERIA

…RETURN ALL ‘SIMILAR’ IMAGES

CBIRSIMPLE EXAMPLE

QUERY IMAGE

RETRIEVALSYSTEM

RETRIEVAL RESULTSBASED ON COLOR CONTENT

CBIRQUERY TYPES

SKETCH

EXAMPLE

COLOR

SHAPE

TEXTURE

MORE COMPLEX TYPES EXIST YET ABOVE ARE

MOST FUNDAMENTAL & MOST REGULARLY USED

COMBINES HIGH-TECH ELEMENTSMULTIMEDIA/SIGNAL/IMAGE PROCESSING

COMPUTER VISION/PATTERN RECOGNITION

COMPUTER SCIENCES

(I.E. HUMAN-COMPUTER INTERACTION)

AND MORE TRADITIONAL CONCEPTSPSYCHOLOGY/HUMAN PERCEPTION

INFORMATION SCIENCES (I.E. LIBRARY)

CBIRCONTRIBUTORS

a

a

a

GOVERNMENT (E.G. MUGSHOTS)

ENTERTAINMENT (FILM, TV)

DESIGN/VISUAL ARTS

INDUSTRY (LOGO MANAGEMENT)

SOME CBIR APPLICATION AREAS

CBIRSCENARIOS

MEDICAL IMAGING

ART/CULTURAL HERITAGE

IMPORTANT QUESTION ARISES: “WHY NOT SIMPLY INDEX USING TEXT?”

(YAHOO! HAS HAD SOME SUCCESS WITH THIS)

INTUITIVE, YET USING TEXT ISSIMPLE BUT SIMPLISTIC

TIME CONSUMING – CAN’T AUTOMATE

HIGHLY SUBJECTIVE & USER-DEPENDENT

SUSCEPTIBLE TO TRANSLATION PROBLEMS

CBIRVERSUS TEXT

CBIRBASIC STRUCTURE

FEATUREEXTRACTION

I N D E X

SIMILARITYCALCULATION

GENERATIONOF RESULTS

USERINTERFACE

SIMILARRESULTS

QUERY

FEATUREDESCRIPTIONS

3 BASIC FEATURESCOLOR, TEXTURE, SHAPE

MANY DESCRIPTORSMPEG-7 IS ISO STANDARDREALLY A DESIGN CHOICE

SIMILARITY OPEN TO RESEARCH

LITTLE PERCEPTUAL CONSIDERATION

ON WHAT BASIS ARE THEY SIMILAR?COLOR CONTENT?SHAPE CONTENT?HIGH LEVEL IDEAS (‘MASKS’, ‘GENDER’)?

PERCEPTION IS ALWAYS AN ISSUE

CONSIDER THREE IMAGES

CBIR(DIS)SIMILARITY?

SIMILARITY IS NOT SO SIMPLE

CBIRSIMILARITY

DOMAIN [0,1] CAN BE CALCULATED MANY WAYS

GENERALIZED

MINKOWSKI

CANBERRA

PERCEPTUAL

MEASURE

rrp

kkkd

1

1

||,

jiji

p

k kk

kkd1

,ji

jiji

2

1

25531cos

211,

ji

ji

jiji

d

EFFECTIVE QUERIES INCOLOR, TEXTURE, SHAPE

SIMPLE HYBRID QUERIESDESCRIPTOR SUPERVECTORSWEIGHTED AVERAGE OF (DIS)SIMILARITIES

RELEVANCE FEEDBACKUSER PLACED IN LOOP GIVES BETTER RESULTSSTATISTICAL APPROACHESAPPLY/ADJUST FEATURE WEIGHTS TO

RELEVANT/IRRELEVANT ELEMENTS

CBIRTYPICAL ABILITIES

CBIRSUMMARY

BORN FROM MULTIMEDIA FLOOD TEXT TOO SIMPLE AND LABORIOUS SYSTEMS WORK DECENTLY IN VITRO

QUERY BY SHAPE, COLOR, TEXTURE, EXAMPLE

SHORTCOMINGSNEED RELEVANCE FEEDBACK & PERCEPTUALHYBRID QUERIES DIFFICULT TO CREATESEMANTIC GAP NEEDS TO BE BRIDGED

MPEG-7: IMPORTANT DEVELOPMENT

GOING FORWARD…



MPEG

MOTION PICTURES EXPERT GROUP MPEG-1 MPEG-2 MPEG-4 MPEG-7: ISO/IEC 15938

MULTIMEDIA CONTENT DESCRIPTION INTERFACE

MPEG-21

MPEG-1 & MPEG-2

MPEG-1 (c. 1992)BASIC VIDEO CODING USING DPCM & DCTTARGET: CD-BASED VIDEO & MULTIMEDIAUSE I, B & P-FRAMES IN YUV SPACE

MPEG-2 (c. 1994)SUPERSET OF MPEG-1GOAL: DTV/DSS OR ATM TRANSPORTMINIMUM OF NTSC/PAL QUALITYMORE ERROR RESILIENTSCALABLE – GRACEFUL DEGRADATION

MPEG-4 & MPEG-21

MPEG-4 (c. 1998) TOOLS TO AUTHOR MULTIMEDIA CONTENTTRAFFIC AWARE, ERROR RESILIENTOBJECT-BASED CODINGVERY EFFICIENT FOR LOW BIT-RATES

MPEG-21 (STARTED JUNE 2000)AN OPEN “MULTIMEDIA FRAMEWORK” IDEAADDRESSES DIGITAL RIGHTS MANAGEMENTENHANCED DELIVERY & ACCESS OF DATA FOR

DEVICES ON HETEROGENEOUS NETWORKS

MPEG-7NEW PARADIGM

UNLIKE MPEG-1, MPEG-2, & MPEG-4DOESN’T REPRESENT CONTENT ITSELFMPEG-7 ONLY DESCRIBES CONTENTDIFFICULT CONCEPT FOR SOME TO GRASP

APPLICABLE TO IMAGESVIDEO

INDEPENDENT OFSTORAGEARCHITECTURE

AUDIO & SPEECHTEXT

TRANSPORTCODING

MPEG-7HOW IT DIFFERS

MPEG-1TAKES INPUT FRAMES AND REPRESENTS AS

AN BINARY ENCODED VIDEO BITSTREAM

MPEG-7TAKES VIDEO FRAMES (SAY MPEG-1 FORMAT)

AND DESCRIBES CONTENTS OF EACH FRAME.

FRAME 1: COLOR CONTENT: 20% WHITE, 14% BLUE, SHAPES: BRIDGE, etc.



MPEG-7SCOPE

MPEG-7SCOPE

FEATUREEXTRACTIONALGORITHM

CODINGSCHEME

CONTENTDESCRIPTION

OTHERELEMENTS

. . .

MULTIMEDIA DATA

MPEG-7GOALS

DESCRIBE MULTIMEDIA CONTENTSET OF DESCRIPTORS (D)

RELATIONS BETWEEN DESCRIPTORSSET OF DESCRIPTION SCHEMES (DS)

LANGUAGE DEFINING D’s & DS’sDESCRIPTION DEFINITION LANGUAGE (DDL)BASED ON XML (eXtensible Markup Language)USED TO BUILD UP NEW D’s & DS’s

ENCODING OF D’s FOR EFFICIENCY

MPEG-7SUMMARY-1

STANDARDIZED DESCRIPTIONS APPLIES TO ALL DIGITAL MEDIA

CBIR IS CASE FOR STILL IMAGES

DOES NOT REPRESENT DATA ITSELFDESCRIBES WHAT DATA REPRESENTS

SETS THE BAR FOR SYSTEMSMULTIMEDIA/IMAGE RETRIEVAL SYSTEMS NEED

AT LEAST MPEG-7 CONFORMANCE

MPEG-7SUMMARY-2

DOES NOT ADDRESSSIMILARITYRELEVANCE FEEDBACKFEATURE EXTRACTIONHYBRID QUERY GENERATIONARCHIVE ORGANIZATION

THE ABOVE ISSUES HAVE BEEN PURPOSEFULLY LEFT OPEN FOR INNOVATION

FORGING AHEAD…



SHORTCOMINGS OF CBIR SYSTEMS

ONGOING RESEARCHRELEVANCE FEEDBACKHYBRID QUERY GENERATIONDISTRIBUTED MULTIMEDIA INDEXING

OPEN RESEARCH AVENUES

RESEARCH ISSUES

CBIRSHORTCOMINGS-1

COLORUSUALLY GLOBALHIGH DIMENSIONALITYGAMMA NONLINEARITIES CAUSE PROBLEMS

SHAPECOMPLICATED & DIFFICULTOCCLUSION ISSUES DURING EXTRACTION

TEXTURECOMPLICATED & UNINTUITIVEUSER-SYSTEM RIFT FOR QUERY CREATION

CBIRSHORTCOMINGS-2

PERCEPTUAL ISSUESSUBTLE DIFFERENCES BETWEEN VIEWERSCOLOR-BLIND USERS

SIMILARITY MEASURESNEED TO BE TUNED TO DESCRIPTORS e.g. EUCLIDEAN DISTANCE NOT APPLICABLE IN

NON-EUCLIDEAN DESCRIPTION SPACE

RELEVANCE FEEDBACKPERFORMED AT GLOBAL (IMAGE) LEVELNEED TO ADDRESS SPECIFIC IMAGE ELEMENTS

ONGOING RESEARCH-2

ITERATIVE QUERY REFINEMENTPLACE USER IN LOOP TO ITERATIVELY IMPROVE

RETRIEVAL RATESHIGH-DIMENSIONAL SPACE NEEDS PRUNINGEMPHASIZED FEATURE(S) MUST BE FOUND

TYPICAL APPROACHESSTATISTICAL METHODSFEATURE WEIGHTING

RELEVANCE FEEDBACK

ONGOING RESEARCH-2

FEATURE SELECTIVE INTERFACEWHY CHOOSE IMAGES ON WHOLE? REQUIRES

PROCESSING/STATS TO FIND GOOD FEATURESUSER CAN EXPLICITLY INDICATE ELEMENTS OF

IMAGE WHICH ARE GOOD: NO GUESSWORK

RELEVANT COLOR

RELEVANT SHAPE

EXPLICIT FEATURES TO R.F. ENGINE

RELEVANCE FEEDBACK

ONGOING RESEARCH-3

TYPICALLY USED APPROACHESBOOLEAN (AND, OR & NOT OPERATORS)EUCLIDEAN (MINKOWSKI W/ r=1)WEIGHTED AVERAGE (WA) i.e. SUPERVECTORS

DISADVANTAGESEUCLIDEAN: FCN OF DESCRIPTORS – CHANGE

DESCRIPTOR, DRASTICALLY ALTER MEASUREWA: INFLEXIBLE FOR HIGH LEVEL QUERIES,

SUPERVECTORS IMPOSE CERTAIN STRUCTUREBOOLEAN: HARD LIMITED TO LOGIC FCNs ALL LACK PERCEPTUAL CONSIDERATIONS

SIMILARITY AGGREGATION/HYBRID QUERIES

FUZZY AGGREGATION OF DECISIONSUSE MEMBERSHIP FUNCTION TO ‘FUZZIFY’

DISTANCES & GENERATE A ‘FUZZY DECISION’

EXPONENTIAL MODELS HUMAN PERCEPTION

ONGOING RESEARCH-4


FUZZYMEMBERSHIP

FUNCTIONSIMILARITY DISTANCE

dFUZZY DISTANCE

DECISION

INDEXES USUALLY CENTRALIZEDENTIRE SYSTEM FAILS IF COMPONENT FAILSNO GRACEFUL PERFORMANCE DEGRADATIONHIGH DATA VOLUME = HIGH SYSTEM REQ’S

DISTRIBUTED INDEXESSPREAD WORKLOAD OVER MANY SUBSYSTEMS INCREASE REDUNDANCYP2P SYSTEMS LACK CENTRALIZED ELEMENTSP2P SYSTEMS RESEMBLE SOCIAL NETWORKS

ONGOING RESEARCH-5

DISTRIBUTED MULTIMEDIA INDEXING

SMALL WORLD INDEXING MODEL1

SOCIOLOGICAL PEER DESCRIPTIONSWE ARE NOT BLIND TO WHO OUR PEERS AREPEOPLE KEEP MEMORY OF THEIR PEERSWE ARE NOT BLIND TO HOW OUR PEERS ARE

WE REFER OTHERS TO OUR PEERS

EXAMPLE

ONGOING RESEARCH-6


[1] P. Androutsos, D. Androutsos and A. N. Venetsanopoulos, “A distributed fault-tolerant MPEG-7 retrieval scheme based on small world theory”, Distributed Media Technologies and Applications Special Issue of IEEE Transactions on Multimedia, under review.

INDEX AND ARCHIVE BECOME ONESWIM DATA STORED IN ARCHIVE OBJECTSEACH DATA OBJECT BEHAVES AS OWN AGENTAGENTS ARE EFFECTIVE IN HIGHLY

NETWORKED ENVIRONMENTS (SWIM)

RETRIEVALSAGENT BASED RETRIEVALUSE OF REFERRAL BASED TECHNIQUE SIMILAR

TO ‘SIX DEGREES OF SEPARATION’CURRENTLY PERFORMED WITH IMAGES

ONGOING RESEARCH-7


ONGOING RESEARCH-8

DISTRIBUTED MULTIMEDIA INDEXING2

[2] P. Androutsos, D. Androutsos and A. N. Venetsanopoulos, “Graceful image retrieval performance degradation using small world distributed indexing”, International Conference on Image Processing ICIP2005, Genoa, Italy.

RESEARCH AVENUES-1

HYBRID QUERIES & AGGREGATIONWHAT DO WEIGHTS MEAN? HOW TO CHOOSE?ALTERNATIVE AGGREGATIONS METHODSADAPTIVE SCHEMES USING REL. FEEDBACK

USER INTERFACEBRIDGE SEMANTIC GAP BETWEEN USER’S IDEA,

AND ABILITY TO EXPRESS AS A QUERYALTERNATIVE INTERFACES–ICONIC, SEMANTIC

RESEARCH AVENUES-2

PERCEPTUAL ISSUESEMPHASIS OF DOMINATING FEATURESFEATURE MASKINGEMOTIONAL INDEXING/ALL USERS DIFFERENT–CUSTOMIZED PROFILE

ARCHIVE DEPENDENCESYSTEMS USUALLY SPECIALIZEDADAPTIVE INDEXING – MOST APPROPRIATE

SYSTEM USED BASED ON PRELIMINARY SURVEY OF CANDIDATE DATABASE

RESEARCH AVENUES-3

DISTRIBUTED INDEXINGDISTRIBUTED INDEXES & RETRIEVAL INDEX SYNCHRONIZATIONRESULTS ORGANIZATION & RANKINGSWIM OVERHEAD ESTIMATIONEXTENSION OF SWIM TO OTHER DATA TYPES

INCORPORATE TEXT METHODSTEXT-INDEXING USING LIMITED VOCABULARYDON’T REJECT BUT USE INTELLIGENTLY

EXTEND TO MPEG-21 & METADATA

SUMMARY-1

MULTIMEDIA PROCESSINGRESULTS FROM MULTIMEDIA EXPLOSIONUSERS DEMANDING MORE FROM DEVICESDEVICES ARE CONVERGING

CONTENT BASED IMAGE RETRIEVALNECESSARY TO TRACK VISUAL SEA OF DATAGOOD CAPABILITIES, BUT W/ SHORTCOMINGSPERCEPTUAL/SUBJECTIVE ISSUESRELEVANCE FEEDBACKDISTRIBUTED CONCEPTS BECOMING CRITICAL

SUMMARY-2

MPEG-7AIMED AT STANDARDIZING DESCRIPTIONSRADICALLY DIFFERENT THAN PREVIOUS MPEGsDDL IS AN EXTENSION OF XML SCHEMAAPPLICABLE TO ALL MULTIMEDIA DATA

ALWAYS MORE TO DO MPEG-7 HAS LEFT MANY ISSUES OPENCBIR NEEDS TO ADDRESS USERS, PERCEPTION,

HYBRID QUERIES, DISTRIBUTED SYSTEMS, ETCVIBRANT RESEARCH COMMUNITY

THANK YOU

HIGH FLEXIBILITY RESULTS IN RISE IN DATA GENERATION & STORAGE INCREASE IN BANDWIDTH NEEDSONE TOOL DOING WORK OF MANY

MANY TYPES OF NETWORKS CAUSECOMPLEX HARDWARE COMBINATIONSONE DEVICE CONNECTING TO ALL NETWORKS

SMALL, PORTABLE DEVICESMINIATURIZATED WITH HUGE CAPABILITIESONE DEVICE REPLACES MANY

IMPACT OF MULTIMEDIA

CBIRWHO’S WHO

COMMERCIAL

ACADEMIC

EXISTING SYSTEMS

QBI C, VI RAGE

PHOTOBOOK, PI C-TO-SEEK,

COMMERCIAL

GOVERNMENT

USERS

TT TV, ART GALLERI ES, WWW FI LTERI NG, DESI GN, MEDI CI NE

SATELLI TE, LEGAL, CORPORATE LOGO

DEFINEDVIA DDL

DEFINED IN MPEG-7STANDARD

MPEG-7D, DS, & DDL

DDL

D

D

DS

DS D

D

DS

D

BUILDING MORE Ds & DSs USING THE DDL

MPEG-7COMPONENTS

SYSTEMS DDL VISUAL

PRIMARY CONCERN FOR THIS PRESENTATION

AUDIO MULTIMEDIA DESCRIPTION SCHEMES EXPERIMENTATION MODEL (XM) CONFORMANCE

MPEG-7VISUAL COMPONENT

BASIC DESCRIPTORS GRID LAYOUT 2D/3D VIEW TIME SERIES SPATIAL 2D COORDS TEMPORAL INTERPOLATION

COLOR DESCRIPTORS COLOR SPACE COLOR QUANTIZATION DOMINANT COLOR SCALABLE COLOR COLOR STRUCTURE COLOR LAYOUT GoF/GoP COLOR

OTHER FACE RECOGNITION

TEXTURE DESCRIPTORS EDGE HISTOGRAM HOMOGENEOUS TEXTURE TEXTURE BROWSING

SHAPE DESCRIPTORS REGION-BASED CONTOUR-BASED 3D SHAPE

MOTION DESCRIPTORS CAMERA MOTION MOTION TRAJECTORY PARAMETRIC MOTION MOTION ACTIVITY

LOCALIZATION SPATIO-TEMPORAL REGION LOCATOR

HIGHLIGHTED DESCRIPTORS USED BY UofT

FUZZY AGGREGATION OF DECISIONSAGGREGATE DECISIONS USING LOGICUSE COMPENSATIVE OPERATORPARAMETER CONTROLS DEGREE OF ANDNESS

(max) & ORNESS (min)

RESULT IS A SINGLE VALUE IN [0,1] INDICATING OVERALL IMAGE SIMILARITY

ONGOING RESEARCH

),max()1(),min( jijiji


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