internet video search a brief history of television...grey value as well as opponent color sets....

Cees Snoek & Arnold SmeuldersUniversity of Amsterdam

29‐11‐2011

Internet Video Search 1

Internet Video SearchInternet Video Search

CeesCees G.M. G.M. SnoekSnoek & Arnold W.M. & Arnold W.M. SmeuldersSmeulders

Intelligent Systems Lab Amsterdam,U i it f A t d Th N th l dUniversity of Amsterdam, The Netherlands

A brief history of televisionA brief history of television

•• From broadcasting to narrowcastingFrom broadcasting to narrowcasting

•• …to …to thincastingthincasting

~1955 ~1985 ~2005

~>2008

~2010

…as of May 2011

The international business caseThe international business case

•• Everybody with a message uses video for deliveryEverybody with a message uses video for delivery

•• Growing Growing unmanageableunmanageable amounts of videoamounts of video

CrowdCrowd--given searchgiven search

What others say is in the video.

RawRaw--driven searchdriven search

www.science.uva.nl/research/isla

MultimediaN project


29‐11‐2011


1. Short course outline1. Short course outline

.0 Problem statement.0 Problem statement

.1 Measuring features.1 Measuring features

.2 Concept detection.2 Concept detection

.3 Lexicon learning.3 Lexicon learning

.4 Telling stories.4 Telling stories

.5 Video browsing.5 Video browsing

Problem 1:Problem 1:Variation in appearanceVariation in appearance

So many images of one thing, due to minor differences in:illuminationbackgroundbackground occlusionviewpoint, …

•• ThisThis is the is the sensorysensory gapgap

SuitBasketball

Tree

1101011011011011011011001101011011111001101011011111

1101011011011011011011001101011011111001101011011111

1101011011011011011011001101011011111001101011011111

1101011011011

110101101101101101101100110101101111100

110101101101101101101100110101101111100

Problem 2:Problem 2:What defines things?What defines things?

Machine

Multimedia Archives

Table

US flag

Aircraft

Dog Tennis Mountain

Fire

Building

011011011001101011011111001101011011111

01011011111001101011011111

1101011011011011011011001101011011111001101011011111

01011011111001101011011111

1101011011011011011011001101011011111001101011011111

1101011011011011011011001101011011111001101011011111

1101011011011011011011001101011011111001101011011111

1101011011011011011011001101011011111001101011011111Humans

Problem 3: Problem 3: Many things in the worldMany things in the world

•• ThisThis is the model gapis the model gap

ProblemProblem 4:4:WhatWhat story story tellstells a video?a video?

•• This is the narrative gapThis is the narrative gap

Problem 5: Problem 5: Use is openUse is open--endedended

scope

•• ThisThis is the interface gapis the interface gap

screen

keywords


29‐11‐2011


Conclusion on problemsConclusion on problems

•• Video search is a diverse and challengeVideo search is a diverse and challenge--rich rich research topicresearch topic

Sensory gapSensory gap–– Sensory gapSensory gap

–– SemanicSemanic gapgap

–– Model gapModel gap

–– Narrative gapNarrative gap

–– Interface gapInterface gap

Today’s promiseToday’s promise

•• You will be acquainted with the theory and You will be acquainted with the theory and practice of the semantic video search paradigm. practice of the semantic video search paradigm.

•• You will be able to recall the five major scientific You will be able to recall the five major scientific problems in video retrieval and explain, and value problems in video retrieval and explain, and value the presentthe present--day solutions.day solutions.



.1 Measuring features.1 Measuring featuresgg





There are a million appearances to one concept

A million appearancesA million appearances

Where are the patterns (of the same shoe)?

Somewhere the variance must be removed.

Invariance: the need for ~Invariance: the need for ~

The illumination and the viewing direction are removed as soon as the image has entered.

Common transformationsCommon transformations

Illumination transformationsIllumination transformations ContrastContrast

Intensity and ShadowIntensity and Shadow

ColorColor

ViewpointViewpoint Rotation and LateralRotation and Lateral

DistanceDistance

Viewing angleViewing angle

ProjectionProjection

CoverCover SpecularSpecular or matteor matte

Occlusion & Clutter, Wear & Tear, Aging, Night & day and Occlusion & Clutter, Wear & Tear, Aging, Night & day and so on into increasingly complex transformations.so on into increasingly complex transformations.


29‐11‐2011


Features of selected points may be good enough to describe object

More than one transformationMore than one transformation

iff the selection & the feature set are both invariant forscene-accidental conditions Gevers TIP 2000

Design of invariants: OrbitsDesign of invariants: Orbits

For a property variant under W, observations of a constant property are spread over the orbit. The purpose of an invariant is to capture all of the orbit into one value.

Example: invarianceExample: invariance

projection

Slide credit: Theo Gevers

(R,G,B)-space(c(c11,c,c22,c,c33))--spacespace

},max{arctan),,(1 BG

RBGRc =

},max{arctan),,(2 BR

GBGRc =

},max{arctan),,(3 GR

BBGRc =

shadows shading highlights ill. intensity ill. shadows shading highlights ill. intensity ill. colorcolorEE -- -- -- -- --WW -- + + -- + + --

Color invarianceColor invariance

Gevers, PRL, 1999Geusebroek, PAMI 2001

CC + + + + -- + + --MM + + + + -- + ++ +NN + + + + -- + ++ +LL + + + + + + + + --HH + + + + + + + + --

Local shape motivationLocal shape motivation

Perceptual importanceConcise data

Robust to occlusion & clutter

Tuytelaars FTCGV 2008

Meet the GaussiansMeet the Gaussians

Taylor expansion at xTaylor expansion at x

For discretely sampled signal use the Gaussians

Robust additive differentialsRobust additive differentials

Dimensions separableDimensions separable

No maxima No maxima introducedintroduced

For discretely sampled signal use the Gaussians


29‐11‐2011


Meet the Gaussians Meet the Gaussians

The basic video observables are:

Local receptive fields Local receptive fields ff((xx))

The receptive fields up to The receptive fields up to second second order.order.

Grey value as well as opponent color sets.Grey value as well as opponent color sets.

Taxonomy of image structureTaxonomy of image structure

Slide credit: Theo Gevers

T-junction

Highlight

Corner

Junction

From receptive fields to meaningFrom receptive fields to meaning

Lee et al, Comm. ACM 2011

With examples

The 2D Gabor function is:

)(22 222

1)( vyuxjyx

eeyxh ++

−= πδ

Meet GaborMeet Gabor

)(222

),( yjeeyxh = δπσ

Tuning parameters: u, v, σ + usual invariants by combinationManjunath and Ma on Gabor for texture as seen in F-space

Local receptive fields Local receptive fields FF((xx))

The receptive fields for (u, v) measured locallyThe receptive fields for (u, v) measured locally

Greyvalue as well as opponent color sets.Greyvalue as well as opponent color sets.

Hoang 2003


29‐11‐2011


GaborGabor filters: filters: texturetexture

Hoang, ECCV 2002

Original image K-means clustering Segmentation

GaborGabor filters: filters: texturetexture

Local receptive field in f(Local receptive field in f(xx,t),t)

Gaussian equivalent over x and t:Gaussian equivalent over x and t:

zero orderzero order first order over tfirst order over t

Burghouts TIP 2006

Receptive fields: overviewReceptive fields: overview

All observables up to first order All observables up to first order colorcolor, , second order spatial scales, eight second order spatial scales, eight frequency bands & first order in time.frequency bands & first order in time.

Good observables > easy algorithmsGood observables > easy algorithms

Periodicity:

Detect periodic motion by one steered filter:

Deadly simple algorithm…

Burghouts TIP 2006

Meet the LoweansMeet the Loweans

So far we paid respect to the spatial order.So far we paid respect to the spatial order.

Now we will weakly follow the spatial order and form Now we will weakly follow the spatial order and form histograms on all directions we encounter locallyhistograms on all directions we encounter locallyhistograms on all directions we encounter locally, histograms on all directions we encounter locally, … better known as (the second part of) SIFT.… better known as (the second part of) SIFT.

Lowe IJCV 2004


29‐11‐2011


Meet the Meet the LoweansLoweans

4 x 4 Gradient window after thresholding4 x 4 Gradient window after thresholding

Histogram of 4 x 4 samples per window in 8 directionsHistogram of 4 x 4 samples per window in 8 directions

Gaussian weighting around center (Gaussian weighting around center (σσ is 1/2 of is 1/2 of σσ keypoint)keypoint)

4 x 4 x 8 = 128 dimensional feature vector4 x 4 x 8 = 128 dimensional feature vector

Image: Jonas Hurrelmann

SIFT detectionSIFT detection

Slide credit: Jepson 2005

Enriching SIFT Enriching SIFT (in a nutshell)(in a nutshell)

•• Affine SIFTAffine SIFT–– Choose prominent direction in SIFTChoose prominent direction in SIFT

Mikolajczyk, IJCV 2005Ke, CVPR 2004

Van de Sande, PAMI 2010

•• PCAPCA--SIFTSIFT–– Robust and compact representationRobust and compact representation

•• ColorSIFTColorSIFT–– Add several invariant color descriptorsAdd several invariant color descriptors

•• TimeSIFTTimeSIFT–– Anyone?Anyone?

Illumination invarianceIllumination invariance

Invariance properties of the descriptors usedLight intensity

changeLight intensity

shiftLight intensity

change and shiftLight color

changeLight color change

and shift

SIFT + + + + +


SIFT + + + + +OpponentSIFT +/- + +/- +/- +/-C-SIFT + + + +/- +/-rgSIFT + + + +/- +/-RGB-SIFT + + + + +

Where to sample in the video?Where to sample in the video?

•• Video shot is set of frames representing a Video shot is set of frames representing a continuous camera action in time and spacecontinuous camera action in time and space

Analysis typically on a single key frame per shotAnalysis typically on a single key frame per shot–– Analysis typically on a single key frame per shotAnalysis typically on a single key frame per shot

Shot Key Frame

WhereWhere to sample in the frame?to sample in the frame?

Tuytelaars 2008 FTCGV


29‐11‐2011


Interest point examplesInterest point examples

Original image Harris Laplacian Color salient points

Mikolajczyk, CVPR 2006van de Weijer, PAMI 2006

DenseDense sampling sampling exampleexample

What What is the object in the is the object in the middle?middle?

No segmentation …No pixel values of the object …

FastFast densedense descriptorsdescriptors

Uijlings et al, CIVR 2009

Image Patch

Reuse sub-regions: 16x speed-up

Conclusion on Conclusion on measuring featuresmeasuring features

•• Invariance is crucial when designing featuresInvariance is crucial when designing features–– More invariance means less stable…More invariance means less stable…

b t b t tb t b t t–– …but more robustness to sensory gap…but more robustness to sensory gap

•• Effective features strike a balance between Effective features strike a balance between invariance and discriminatory powerinvariance and discriminatory power–– And for video search efficiency is helpful also…And for video search efficiency is helpful also…

And there is always more …And there is always more …

For example:For example:

Local Invariant Feature Detectors: A SurveyLocal Invariant Feature Detectors: A Survey

TinneTinne TuytelaarsTuytelaars & & KrystianKrystian MikolajczykMikolajczyk

FTCGV 3:3(177FTCGV 3:3(177——280)280)


29‐11‐2011









The semantic gapThe semantic gap

The semantic gap is the lack of coincidenceThe semantic gap is the lack of coincidence

Quote

The semantic gap is the lack of coincidence The semantic gap is the lack of coincidence between the information that one can extract between the information that one can extract from the sensory data and the interpretation that from the sensory data and the interpretation that the same data has for a user in a given situationthe same data has for a user in a given situation

Arnold Smeulders, PAMI, 2000

The science of labelingThe science of labeling

•• To understand anything in science, things need a To understand anything in science, things need a name that is universally recognizedname that is universally recognized

•• Worldwide endeavor in Worldwide endeavor in naming visual informationnaming visual information

living organisms chemical elements human genome‘categories’ text

Naming visual informationNaming visual information

•• Concept detectionConcept detection–– Does the image contain an airplane?Does the image contain an airplane?

Slide credit: Andrew Zisserman

Focus of today’s Focus of today’s lecturelecture

•• Object localizationObject localization–– Where is the airplane, (if any)?Where is the airplane, (if any)?

•• Object segmentationObject segmentation–– Which pixels are part of an airplane, Which pixels are part of an airplane,

(if any)?(if any)?

How difficult is the problem?How difficult is the problem?

•• Human vision consumes 50% brain power…Human vision consumes 50% brain power…

Van Essen, Science 1992

Semantic concept detectionSemantic concept detection

•• The patient approachThe patient approach–– Building detectors oneBuilding detectors one--atat--aa--timetime

A face detector for frontal faces


29‐11‐2011


A simple face detectorA simple face detector

One PhD per detector requires too many students…

So how about these?So how about these?

and the thousands of others ………

Labeled

Basic concept detectionBasic concept detection

aircraftoutdoor

Feature Extraction

Supervised Learner

Training

Feature Measurement

Classification

Testing

Video

Labeled examples

It is an aircraft probability 0.7It is outdoor probability

0.95

Demo: concept detectionDemo: concept detection

Visualization byJasper Schulte

Linear classifiers Linear classifiers -- marginmargin

Slide credit: Cordelia Schmid

Nonlinear SVMNonlinear SVM



29‐11‐2011


Breakthrough: nonlinear SVMsBreakthrough: nonlinear SVMs

Vapnik, 1995

Nonlinear SVMsNonlinear SVMs


Kernels for concept detectionKernels for concept detection


Comparing kernelsComparing kernels

Zhang, IJCV ‘07

Causes of poor generalizationCauses of poor generalization

•• OverOver--fittingfitting–– Separate your dataSeparate your data

•• Curse of dimensionalityCurse of dimensionality–– Information fusion helpsInformation fusion helps

Feature fusionFeature fusionSynchronization

Normalization

Transformation

Concatenation

Feature vector

Shot segmentedvideo

Concept confidence

Concatenation


29‐11‐2011


Feature fusionFeature fusionSynchronization

Normalization

Transformation

Concatenation

Feature vector

Shot segmentedvideo

Concept confidence

Concatenation

+ Only one learning phase- Combination often ad hoc - One feature may dominate- Curse of dimenisonality

Avoiding dimensionality curseAvoiding dimensionality curse

•• Codebook aka bagCodebook aka bag--ofof--words modelwords model–– Create a codeword vocabularyCreate a codeword vocabulary

Di tiDi ti i ithi ith d dd d

Leung and Malik, IJCV, 2001Sivic and Zisserman, ICCV, 2003

and many others…

–– DiscretizeDiscretize image with image with codewordscodewords

–– Count codewordsCount codewords

0 100 200 300 400 5000

10

20

30

40

50

60

70

80

EmphasizingEmphasizing spatialspatial configurationsconfigurations

•• CodebookCodebook ignoresignores geometricgeometric correspondencecorrespondence

Grauman, ICCV 2005Lazebnik, CVPR 2006

Marszalek, VOC 2007

•• For video:For video:

•• SolutionSolution: : spatialspatial pyramidpyramid–– aggregate statistics of local features over fixed aggregate statistics of local features over fixed

subregionssubregions

–– 1x1 entire image1x1 entire image

–– 2x2 image quarters2x2 image quarters

–– 1x3 horizontal bars1x3 horizontal bars

Codebook modelCodebook model

•• Codebook consists of Codebook consists of codewordscodewords–– kk--means clustering of descriptorsmeans clustering of descriptors–– Commonly 4 000Commonly 4 000 codewordscodewords per codebookper codebookCommonly 4,000 Commonly 4,000 codewordscodewords per codebookper codebook

Cluster

AssignDense+OpponentSIFT Feature vector (length 4,000)

Codebook assignmentCodebook assignment

van Gemert, PAMI 2010

Hard assignmentHard assignment Soft assignmentSoft assignment

● Codeword

–– Soft assignment: assign to multiple clusters, weighted by Soft assignment: assign to multiple clusters, weighted by distance to centerdistance to center

–– Single sigma (distance weighting) for all codebook elementsSingle sigma (distance weighting) for all codebook elements

ExtendingExtending soft soft assignmentassignment

•• FisherFisher VectorVector–– Train a Train a GaussianGaussian Mixture Model, Mixture Model, wherewhere eacheach codebookcodebook

element haselement has itsits ownown sigmasigma –– oneone per dimensionper dimension

Perronnin ECCV 2010

element has element has itsits ownown sigma sigma oneone per dimensionper dimension

–– Do Do notnot store store assigmentassigment, , butbut differencesdifferences in all descriptor in all descriptor dimensionsdimensions

•• GreatlyGreatly increasesincreases complexitycomplexity–– feature vector is feature vector is #codewords x #descriptor#codewords x #descriptor


29‐11‐2011


ExtendingExtending soft soft assignmentassignment

•• Super Vector CodingSuper Vector Coding–– also counts the dimensionalso counts the dimension--wise difference of a SIFT wise difference of a SIFT

descriptor to a visual worddescriptor to a visual word

Zhou ECCV 2010

descriptor to a visual worddescriptor to a visual word

•• Key insight: these methods propose many new Key insight: these methods propose many new components and algorithms, but components and algorithms, but difference difference coding coding is their main contributionis their main contribution

Difference codingDifference coding

•• Fisher vectorFisher vector

Perronnin ECCV 2010Zhou ECCV 2010

Jegou CVPR 2010

•• Super vector codingSuper vector coding

•• VLADVLAD

Fast quantizationFast quantization

•• Random Random forestsforests–– Randomized process makes it very fast to buildRandomized process makes it very fast to build

T t t ll f t t ti tiT t t ll f t t ti ti

Moosman, PAMI 2008Uijlings, CIVR 2009

–– Tree structure allows fast vector quantizationTree structure allows fast vector quantization

–– Logarithmic rather than linear projection timeLogarithmic rather than linear projection time

•• RealReal--timetime BoWBoW (!)(!)–– WhenWhen usedused withwith fastfast densedense samplingsampling

–– SURF 2x2 descriptor SURF 2x2 descriptor insteadinstead of 4x4of 4x4

–– RBF RBF kernelkernel

Codebooks grow big…Codebooks grow big…

•• Researchers concatenate multiple codebooksResearchers concatenate multiple codebooks

Spatial pyramid adds more dimensionsSpatial pyramid adds more dimensionsoo 1x1 = 4K1x1 = 4K

oo 2x2 = 16K2x2 = 16K

oo 1x3 = 12K1x3 = 12K

–– Feature vector length easily Feature vector length easily >100K>100K……

SVM preSVM pre--computed kernel trickcomputed kernel trick

•• Use distance between feature vectorsUse distance between feature vectors

-γ dist(f1, f2)K(f f ) = e

•• Increase efficiency significantlyIncrease efficiency significantly–– PrePre--compute the SVM kernel matrixcompute the SVM kernel matrix

–– Long vectors possible as we only need 2 in memoryLong vectors possible as we only need 2 in memory

–– Parameter optimization reParameter optimization re--uses preuses pre--computed matrixcomputed matrix

K(f1, f2) = e

GPUGPU--empowered empowered prepre--computed kernelcomputed kernel

1 CPU 4 CPU

Van de Sande, TMM 2011

40000 1x CPU Opteron 250 (2,4GHz)

1x CPU Core i7 920 (2 66GHz)

GPU0

10000

20000

30000

4000 8000 16000 32000 64000 128000

Time (s)

Total Feature Vector Length

1x CPU Core i7 920 (2,66GHz)

4x CPU Opteron 250 (2,4 GHz)

4x CPU Core i7 920 (2,66GHz)

25x CPU Opteron 250 (2,4GHz)

49x CPU Opteron 250 (2,4GHz)

1x GPU Geforce GTX260 (27 cores)

37x speed-upsingle CPU

10x speed-upquad core

2x speed-up49cpu cluster


29‐11‐2011


Efficient classificationEfficient classification

Maji et al., CVPR 2008

For the Intersection Kernel hi is piecewise linear, and quite smooth, blue plot. We can approximate with fewer uniformly spaced segments, red plot. Saves time & space!

vsvs HIKHIKHIK 75 times faster, negligible loss in average precision

χ²χ²

Moving object appearanceMoving object appearance

= Emphasis added

keyframeShot boundary Shot boundary

Prob

abili

ty

0

1

Moving object appearanceMoving object appearance

= Emphasis added

keyframeShot boundary Shot boundary

Prob

abili

ty

0

1Max

Avg

Feature Feature fusionfusion

0

1

Relative frequency

1 2 3 4 5

Codebook element

Harris -Laplace salient points

Point sampling strategy Color feature extraction Codebook model

Bag-of-features

.

.

.


Spatial pyramid

Dense sampling

0

1

1 2 3 4 5

0

1

1 2 3 4 5

0

1

1 2 3 4 5

0

1

1 2 3 4 5

0

1

Relative frequency

1 2 3 4 5

Codebook element

Bag-of-features

Spatial pyramid: multiple bags-of-features

.

Image

.

.

.

.

..

+ Codebook reduces dimensionality- Combination still ad hoc - One codebook may dominate

Classifier Classifier fusionfusion

+ Focus on feature strength

+ Fusion in semantic spacep

- Expensive learning effort

- Loss of feature correlation


29‐11‐2011


Unsupervised fusion of classifiersUnsupervised fusion of classifiers

Support Vector

Global Image

+ Aggregation functions reduce learning effort+ Efficient use of training examples

Snoek, TRECVID 2006Wang, ACM MIR 2007

Vector MachineFeature Extraction

GeometricMean

Logistic Regression

Fisher Linear

Discriminant

Regional Image Feature

Extraction

Keypoint Image Feature

Extraction

- Linear function unlikely to be optimal

Fusing conceptsFusing concepts

•• Exploitation of concept coExploitation of concept co--occurrence occurrence –– Concepts do not occur in vacuumConcepts do not occur in vacuum

Concept 1

Concept 2

Concept 3

Naphade Trans. MM 2001

SkyAircraft

HowHow to to fusefuse conceptsconcepts??

•• LearningLearning modelsmodels

•• IncludeInclude ontologiesontologies

Learning models Learning models -- explicitlyexplicitly

•• Using graphical modelsUsing graphical models–– Computationally complexComputationally complex

Li it d l bilitLi it d l bilit–– Limited scalabilityLimited scalability

Qi, TOMCCAP 2009

Learning models Learning models -- implicitlyimplicitly

•• Using support vector machine, or data miningUsing support vector machine, or data mining–– Assumes classifier learns relationsAssumes classifier learns relations

S ff f tiS ff f ti–– Suffers from error propagationSuffers from error propagation

Weng, ACM MM 2008

Including knowledgeIncluding knowledge

•• Can ontologies help?Can ontologies help?–– Symbolic ontolgoies Symbolic ontolgoies vsvs uncertain detectorsuncertain detectors

Wu, ICME 2004


29‐11‐2011


Concept detection pipelineConcept detection pipeline

IBM 2003

Concept detection pipelineConcept detection pipeline

IBM 2003

FeatureFusion

ClassifierFusion

ConceptFusion

Video diverVideo diver

Wang, ACM MIR 2007

Video diverVideo diver

FeatureFusion

ClassifierFusion

ConceptFusion

Wang, ACM MIR 2007

Supervised L

Supervised Learner

Semantic Features

Combination

Content Features Extraction

Layout Features Extraction

Semantic PathfinderSemantic Pathfinder

Snoek, PAMI 2006

Supervised Learner

Multimodal Features

Combination

Learner

Visual Features Extraction

Textual Features Extraction

Content Analysis Step Style Analysis Step Context Analysis Step

Context Features Extraction

Capture Features Extraction

Select Best of 3 Paths

after Validation

Animal

Sports

Vehicle

Entertainment Monologue

FlagFire

Weather news

Hu Jintao

Supervised L

Supervised Learner

Semantic Features

Combination

Content Features Extraction

Layout Features Extraction

Semantic PathfinderSemantic PathfinderFeatureFusion

ClassifierFusion

ConceptFusion

Snoek, PAMI 2006

Supervised Learner

Multimodal Features

Combination

Learner

Visual Features Extraction

Textual Features Extraction

Content Analysis Step Style Analysis Step Context Analysis Step

Context Features Extraction

Capture Features Extraction

Select Best of 3 Paths

after Validation

Animal

Sports

Vehicle

Entertainment Monologue

FlagFire

Weather news

Hu Jintao


29‐11‐2011


StateState--ofof--thethe--ArtArt

Snoek et al, TRECVID 2008-2011Van Gemert et al, PAMI 2010

Van de Sande et al, PAMI 2010

StateState--ofof--thethe--ArtArtFeatureFusion

ClassifierFusion

Snoek et al, TRECVID 2008-2011Van Gemert et al, PAMI 2010

Van de Sande et al, PAMI 2010

StateState--ofof--thethe--ArtArt

Snoek et al, TRECVID 2008-2011Van de Sande et al, PAMI 2010

Van Gemert et al, PAMI 2010

Software available for download at http://colordescriptors.com

Demo: Demo: MediaMillMediaMill video search enginevideo search engine

http://www.mediamill.nlhttp://www.mediamill.nl

Detecting Semantic Concepts in VideoDetecting Semantic Concepts in VideoConclusion on:Conclusion on:

•• We started with invariance and manual laborWe started with invariance and manual labor

•• We generalized with machine learningWe generalized with machine learning–– …but needed several abstractions to do appropriately…but needed several abstractions to do appropriately

•• For the moment, no oneFor the moment, no one--sizesize--fitsfits--all solution all solution –– Learn optimal machinery per conceptLearn optimal machinery per concept









29‐11‐2011


Problem 3: Problem 3: Many things in the worldMany things in the world

•• ThisThis is the model gapis the model gap

Trial 1: counting dictionary wordsTrial 1: counting dictionary words

Biederman, Psychological Review 1987

Slide credit: Li Fei-Fei

Trial 2: reverseTrial 2: reverse--engineeringengineering

•• Estimation by Hauptmann et al.: 5000Estimation by Hauptmann et al.: 5000–– Using manually labeled queries and conceptsUsing manually labeled queries and concepts

But speculative and questionable assumptionsBut speculative and questionable assumptions

Hauptmann, PIEEE 2008

–– But speculative, and questionable assumptionsBut speculative, and questionable assumptions

‘Google performance’

Oracle Combination + Noise

‘Realistic’ Combination

How to obtain labeled examples?How to obtain labeled examples?massive amounts of

–– …but only human …but only human expertsexperts provide provide good qualitygood quality examplesexamples

•• MM078MM078--Police/Security PersonnelPolice/Security Personnel–– Shots depicting law enforcement or private Shots depicting law enforcement or private

security agency personnel.security agency personnel.

Experts start with concept definitionExperts start with concept definition

y g y py g y p

Expert annotation toolsExpert annotation tools

•• Balance between:Balance between:–– Spatiotemporal level of Spatiotemporal level of

annotation detailannotation detail

Volkmer, ACM MM 2005

annotation detailannotation detail

–– Number of conceptsNumber of concepts

–– Number of positive Number of positive and negative examplesand negative examples


29‐11‐2011


LSCOM LSCOM (Large Scale Concept Ontology for Multimedia)(Large Scale Concept Ontology for Multimedia)

•• Provides manual annotations for 449 conceptsProvides manual annotations for 449 concepts–– In international broadcast TV newsIn international broadcast TV news

Naphade, IEEE MM 2006

•• Connection to Connection to CycCyc ontologyontology

•• LSCOMLSCOM--LiteLite–– 39 semantic concepts39 semantic concepts

http://www.lscom.org/

Verified positive examplesVerified positive examples

•• ImageNetImageNet (15M images)(15M images)–– 22,000 22,000 categoriescategories

100 l100 l–– > 100 examples> 100 examples

•• SUN SUN (130K images)(130K images)–– 397 scene categories397 scene categories

–– > 100 examples> 100 examples

Deng et al, CVPR 2009

Xiao et al, CVPR 2010

Random negatives are not Random negatives are not necessarily informativenecessarily informative

PositivesNegatives Decision boundary

Xirong Li et al, ICMR 2011

？Active Learning?AdaBoost?

Social Negative

Bootstrapping

Sampling informative negatives Sampling informative negatives

•• Iteratively selecting the Iteratively selecting the most misclassified most misclassified negatives as the informative onesnegatives as the informative ones

Selection

Prediction*

* airplane classifier

Virtually labeled negatives

Most misclassified negatives

Social negative bootstrappingSocial negative bootstrapping

aviationstation tennis outreach

lotusvagrantsketchpeople

b hlif i ft i l cow

airplaneNegative examples

beachlife aircraft airplane cow

planesister street

tokyolithuania aeroplane aeroplane

****

tradeoff between effectiveness and efficiency

to find the most informative negatives

Concept detection Concept detection (on VOC08(on VOC08--val)val)

•• Social negative bootstrapping is much better Social negative bootstrapping is much better than the baselinesthan the baselines


29‐11‐2011


Informative negatives of ‘Informative negatives of ‘tvtv’’

Tag cloud of informative negatives

Bridging the model gapBridging the model gap

•• RequirementsRequirements–– Generic concept detection methodGeneric concept detection method

M i t fM i t f l b l dl b l d ll–– Massive amounts of Massive amounts of labeled labeled examplesexamples

–– Evaluation methodEvaluation method

–– Fair amount of computationFair amount of computation

Model gap best treated by Model gap best treated by TRECVIDTRECVID

•• Situation in 2000Situation in 2000–– Various concept definitionsVarious concept definitions

S ifiS ifi dd llll d t td t t–– SpecificSpecific and and smallsmall data setsdata sets

–– Hard to compare methodologiesHard to compare methodologies

•• Since 2001 worldwide evaluation by NISTSince 2001 worldwide evaluation by NIST–– TRECVID benchmarkTRECVID benchmark

= ResearchersNIST

NIST TRECVID benchmarkNIST TRECVID benchmark

•• Promote progress in video retrieval researchPromote progress in video retrieval research–– Provide common dataset Provide common dataset –– Challenging tasksChallenging tasks

I d d t l ti t lI d d t l ti t l–– Independent evaluation protocolIndependent evaluation protocol–– Forum for researchers to compare resultsForum for researchers to compare results

http://trecvid.nist.gov/

Video data setsVideo data sets

•• US TV news US TV news (`03/`04)(`03/`04)

•• International TV news International TV news (`05/`06)(`05/`06)

•• Dutch TV infotainment Dutch TV infotainment (`07/`08/`09)(`07/`08/`09)

TRECVID 2010 and 2011TRECVID 2010 and 2011Internet Archive web videosInternet Archive web videos


29‐11‐2011


Expert annotation effortsExpert annotation efforts

LSCOM

MediaMill - UvA

Expert annotation efforts

17 32 39101

374

500

…

LSCOM

Others

TRECVID Edition

Measuring performanceMeasuring performance

Set of retrieved itemsSet of relevant items

1.

Results

•• PrecisionPrecision Set of relevant retrieved items

inverse relationship•• RecallRecall

2.

3.

4.

5.

Evaluation measureEvaluation measure

•• Average PrecisionAverage Precision–– Combines precision and recallCombines precision and recall

A i i ft l t h tA i i ft l t h t

1.

Results

–– Averages precision after relevant shotAverages precision after relevant shot

–– Top of ranked list most important Top of ranked list most important

AP =1/1 + 2/3 + 3/4 + …

number of relevant documents

2.

3.

4.

5.

AP

De facto evaluation standardDe facto evaluation standard

Concept examplesConcept examples

Aircraft

BeachNote the variety in visual appearance

Mountain

People marching

Police/Security

Flower

pp

TRECVID TRECVID concept detection task resultsconcept detection task results

•• Hard to compareHard to compare–– Different video dataDifferent video data

2003

2004

–– Different conceptsDifferent concepts

•• Clear Clear top top performersperformers–– Median skews to leftMedian skews to left

–– Learning effectLearning effect

–– Plenty of variationPlenty of variation

2005

2006

2007

2008

2009

2010

2011


29‐11‐2011


UvAUvA--MediaMillMediaMill@TRECVID@TRECVID

Snoek et al, TRECVID 04-11

• >1000 other systems

MediaMill ChallengeMediaMill Challenge

•• The Challenge providesThe Challenge provides–– Manually annotated lexicon Manually annotated lexicon

of of 101101 semantic conceptssemantic concepts

•• The Challenge allows toThe Challenge allows to–– Gain insight in intermediate Gain insight in intermediate

video analysis stepsvideo analysis steps

http:/www.mediamill.nl/challenge/

–– PrePre--computed lowcomputed low--level level featuresfeatures

–– Trained classifier modelsTrained classifier models

–– 55 experimentsexperiments–– Implementation + resultsImplementation + results

–– Foster repeatability of Foster repeatability of experimentsexperiments

–– Optimize video analysis Optimize video analysis systems on a component levelsystems on a component level

–– Compare and improveCompare and improve

• The Challenge lowers threshold for novice researchers

Columbia374 + VIREO374Columbia374 + VIREO374

•• Baseline detectors for 374 conceptsBaseline detectors for 374 concepts

http://www.ee.columbia.edu/ln/dvmm/columbia374/http://www.cs.cityu.edu.hk/~yjiang/vireo374/http://www.ee.columbia.edu/ln/dvmm/CU-VIREO374/

Community myths or facts?Community myths or facts?

•• Chua et al., Chua et al., ACM Multimedia 2007ACM Multimedia 2007

–– Video search is practically solved and progress Video search is practically solved and progress has only been incrementalhas only been incrementalhas only been incrementalhas only been incremental

•• Yang and Hauptmann, Yang and Hauptmann, ACM CIVR 2008ACM CIVR 2008–– Current solutions are weak and generalize poorlyCurrent solutions are weak and generalize poorly

We have done an experimentWe have done an experiment

•• Two video search engines from 2006 and 2009Two video search engines from 2006 and 2009–– MediaMillMediaMill Challenge 2006 systemChallenge 2006 system

M di MillM di Mill TRECVID 2009 tTRECVID 2009 t–– MediaMillMediaMill TRECVID 2009 systemTRECVID 2009 system

•• How well do they detect 36 LSCOM concepts?How well do they detect 36 LSCOM concepts?

Four video data set mixturesFour video data set mixtures

•• TrainingTrainingBroadcast

newsDocumentary

video

TRECVID 2005 TRECVID 2007

•• TestingTestingDocumentary

videoBroadcast

news

Within domain

Cross domain


29‐11‐2011


Performance doubled in just 3 yearsPerformance doubled in just 3 years

• 36 concept detectors

–– Even when using training Even when using training data of different origindata of different origin

Snoek & Smeulders, IEEE Computer 2010

–– Vocabulary still limitedVocabulary still limited

500 detectors, a closer look500 detectors, a closer look

The number of labeled image examples used at training time seems decisive in concept detector accuracy.

Demo timeDemo time 500 detectors, a closer look500 detectors, a closer look

Learning social tag relevance by Learning social tag relevance by neighbor votingneighbor voting

•• Exploit consistency in tagging behavior of Exploit consistency in tagging behavior of different users for visually similar imagesdifferent users for visually similar images

Xirong Li, TMM 2009

Image retrieval experimentsImage retrieval experiments

•• UserUser--tagged image databasetagged image database–– 3.5 million 3.5 million labeled Flickr imageslabeled Flickr images

•• Visual feature Visual feature –– A global colorA global color--texture texture

•• Evaluation setEvaluation set–– 20 concepts20 concepts

•• Evaluation criteriaEvaluation criteria–– Average precisionAverage precision


29‐11‐2011


Image retrieval experimentsImage retrieval experiments

•• A A standardstandard tagtag--basedbased retrievalretrieval frameworkframework–– RankingRanking functionfunction: OKAPI: OKAPI--BM25BM25

•• ComparisonComparison–– Baseline:Baseline: retrieval using original tagsretrieval using original tags

–– Neighbor: Neighbor: retrieval using learned tag relevance as retrieval using learned tag relevance as tag frequencytag frequency

ResultsResults

24% relative improvement

Updated tag relevanceUpdated tag relevance

•• Objective tags are identified and reinforcedObjective tags are identified and reinforced

Based on 3.5 Million images downloaded from Flickr

Tag relevance fusionTag relevance fusion

Xirong Li et al, CIVR 2010

•• Note: fully unsupervised, adds 10% in performanceNote: fully unsupervised, adds 10% in performance

Failure case: airplaneFailure case: airplane

•• Too much consensus on wrong label…Too much consensus on wrong label…

Results suggest…Results suggest…

•• Relevance of a tag can be predicted based on Relevance of a tag can be predicted based on ‘wisdom’ of crowds‘wisdom’ of crowds

Even with a lightEven with a light weight visual featureweight visual feature–– Even with a lightEven with a light--weight visual featureweight visual feature

–– And, a small database of 3.5M imagesAnd, a small database of 3.5M images


29‐11‐2011


Conclusion on lexicon learningConclusion on lexicon learning

RequiresRequires•• Invariant featuresInvariant features

Suffers most Suffers most fromfrom•• Weakly labeled visual dataWeakly labeled visual data

•• Concept detectionConcept detection

•• Many, many, annotationsMany, many, annotations

•• Measuring performanceMeasuring performance

•• Lots of computationLots of computation

•• Transfer across domainsTransfer across domains

Special issue announcementSpecial issue announcement

Fall 2012








StorytellingStorytelling

Storytelling is the conveying of events in words, Storytelling is the conveying of events in words,

Quotey g y g ,y g y g ,

images and sounds, often by improvisation or images and sounds, often by improvisation or embellishment. Stories or narratives have been embellishment. Stories or narratives have been shared in every culture as a means of shared in every culture as a means of entertainment, education, cultural preservation entertainment, education, cultural preservation and in order to instill moral values.and in order to instill moral values.

Wikipedia

An event in prehistoric timesAn event in prehistoric times

•• What story did they want to tell?What story did they want to tell?

Bhimbetka rock shelters, India

Arbitrary events in modern timesArbitrary events in modern times

•• …but what defines an event in video?…but what defines an event in video?

Attempting a board trickFlash mob gathering Changing a vehicle tire


29‐11‐2011


Definition 1Definition 1

•• Complex activity occurring at a specific place and Complex activity occurring at a specific place and time;time;

•• Involves people interacting with other people Involves people interacting with other people and/or objects;and/or objects;

•• Consists of a number of human actions, Consists of a number of human actions, processes, and activities that are loosely or tightly processes, and activities that are loosely or tightly organized and that have significant temporal and organized and that have significant temporal and semantic relationships to the overarching activity;semantic relationships to the overarching activity;

•• Is directly observable.Is directly observable.

Definition 2Definition 2

•• An event happens over timeAn event happens over time–– Track semantics over time, model dynamic Track semantics over time, model dynamic

interactions and identify goalinteractions and identify goal--directed movementdirected movementinteractions, and identify goalinteractions, and identify goal directed movement.directed movement.

•• An event happens in spaceAn event happens in space–– Identify the most semantic regions in an image to Identify the most semantic regions in an image to

express spatial relations and interactions.express spatial relations and interactions.

•• An event has a recountAn event has a recount–– Must be able to provide a understandable recounting Must be able to provide a understandable recounting

on what visual information is decisive for relevance.on what visual information is decisive for relevance.

An event is a bagAn event is a bag--ofof--featuresfeatures

Jiang et al, TRECVID 2010

Video Event

fid

•• Fusing multiple audioFusing multiple audio--visual features is effectivevisual features is effective–– No notion whether the event is really foundNo notion whether the event is really found

Feature vector

Video confidence

An event is a bagAn event is a bag--ofof--conceptsconcepts

Gkalelis et al, CBMI 2011Merler et al, TMM, in press

Video Eventconfidence

Concept Detector 1

Concept

•• Combine available detector scores in single vectorCombine available detector scores in single vector–– No notion what concept (ordering) is important for eventsNo notion what concept (ordering) is important for events

Concept Detector 2

Concept Detector N

Horse, horse, horse?Horse, horse, horse? Chair, chair, chair?Chair, chair, chair?


29‐11‐2011


An event is concepts over timeAn event is concepts over time

Ebadollahi, ICME 2006

•• Exploit concept evidence over timeExploit concept evidence over time–– Proof of concept, unclear how to determine automaticallyProof of concept, unclear how to determine automatically

The event retrieval paradoxThe event retrieval paradox

cura

cy

Bag-of-

???good

Even

t de

tect

ion

acc

Event recounting ability

features

Bag-of-concepts

Conceptsover time

goodbad

bad

Benchmarking the paradoxBenchmarking the paradox

•• NIST Multimedia event detection taskNIST Multimedia event detection task

•• NIST Multimedia event recounting taskNIST Multimedia event recounting task

Multimedia Event Detection taskMultimedia Event Detection task

•• Given an event specified by an Given an event specified by an –– event kit (query) which consists of: event kit (query) which consists of:

d fi itid fi iti–– definition, definition,

–– event explication, event explication,

–– evidential description, andevidential description, and

–– illustrative examples, illustrative examples,

•• Search video for events.Search video for events.

Example Event Kit Example Event Kit

zE id i l D i i

Definition: One or more people fashion an object out of wood.

Event Explication: Woodworking is a popular hobby that involves crafting an object out

of wood. Typical woodworking projects may range from creating large pieces of furniture to small decorative items or toys. The process for making objects out of wood can include cutting wood into smaller pieces…. (continues)

Mnemonic

Textual Definition

Expresses event domain specific knowledge to understand the event

Event Name: Working on a woodworking project

zEvidential Description: scene: Often indoors in a workshop, garage, artificial lighting.

Occasionally outdoorsobjects/people: Woodworking tools (automatic or non-automatic

saws, sander, knife), paint, stains, sawhorses, toolbox, safety goggles

activities: Cutting and shaping wood, attaching pieces of wood together, smoothing/sanding wood

audio: power tool sounds; hand tool sounds (hammer, saw, etc.); narration of process

definition

Textual listing of attributes that are often associated with the event

Exemplars: HVC334271.mp4, HVC393428.mp4, HVC875424.mp4, etc.

Specific clips from the “Event Kits” data set that are known to contain the event being defined.

Target User: An Internet information analyst or experienced Internet searcher with event-specialized knowledge.

Multimedia Event Recounting taskMultimedia Event Recounting task

•• An event detector will rapidly and automatically An event detector will rapidly and automatically produce a textual Englishproduce a textual English--language recounting of language recounting of each event occurrence it finds in a video collection,each event occurrence it finds in a video collection,–– describing the particular scene, actors, objects, and describing the particular scene, actors, objects, and

activities involved.activities involved.

•• Task starts in 2012Task starts in 2012


29‐11‐2011


Video collectionsVideo collections

Collection Name Designated Uses Target sizes AnnotationPilot 2010

Development collectionTest collection

1,723 clips1,742 clips(100 hours)

Clip content annotation for both sets

Development (DEV) 2011Split into two subsets:

44K clips,(~ 1400

For MED ‘11: Clip content p

(1) Transparent (DEV‐T)(2) Opaque (DEV‐O)

2012‐2015(1) and (2) merged to a single training collection

(hours)

pannotation for the transparent subsetAfter MED ‘11:Clip content annotation for the opaque subset

Progress 2012‐2015: test collection 120K clips,4000 hrs

No clip content annotation

Novel 1 2014: test collection 120K clips,4000 hrs.


Novel 2 2015: test collection 120K clips,4000 hrs.


The TRECVID MED ‘11 eventsThe TRECVID MED ‘11 events

Process‐Observed EventsAttempting a board trickFeeding an animalLanding a fishWorking on a woodworking project

Life EventsWedding ceremony

Trai

ning

Ev

ents

Test

ing

Even

ts Process‐Observed EventsChanging a vehicle tireGetting a vehicle unstuckGrooming an animalMaking a sandwichParkourRepairing an applianceWorking on a sewing project

Life EventsBirthday partyFlash mob gatheringParade

Conclusion on telling storiesConclusion on telling stories

•• Not much to conclude yetNot much to conclude yet–– Due to benchmarks, much will happen coming yearsDue to benchmarks, much will happen coming years

•• Field is currently obsessed with bagField is currently obsessed with bag--ofof--featuresfeatures–– Does not solve the event retrieval Does not solve the event retrieval paradoxparadox

–– Can multimedia help?Can multimedia help?

–– Can concepts help?Can concepts help?

•• The topic is wide openThe topic is wide open–– Perfect for (many) PhD’sPerfect for (many) PhD’s








Problem 5: Problem 5: Use is openUse is open--endedended

scope

•• ThisThis is the interface gapis the interface gap

screen

keywords

Video search 1.0Video search 1.0

Note the influence of textual meta data, such as the video title, on the search results.


29‐11‐2011


Query selectionQuery selection ‘Classic’ Informedia system‘Classic’ Informedia system

•• First multimodal video search engineFirst multimodal video search engine

Carnegie Mellon University

FíschlárFíschlár

•• Optimized for use by “real” usersOptimized for use by “real” users

Dublin City University

IBM iMARSIBM iMARS

•• A web based systemA web based system

IBM Research

http://mp7.watson.ibm.com/

MediaMagicMediaMagic

•• Focus on the story levelFocus on the story level

FxPal

VisionGoVisionGo

•• Extremely fast and efficientExtremely fast and efficient

NUS & ICT-CAS


29‐11‐2011


RankRank

CrossBrowsing through resultsCrossBrowsing through results

Snoek, TMM 2007

TimeTime

Sphere variant

Extreme video retrievalExtreme video retrieval

•• ObservationObservation–– Correct results are retrieved, but not optimally rankedCorrect results are retrieved, but not optimally ranked

If h ti t lt h ti l t i l iIf h ti t lt h ti l t i l i

= very demanding!= very demanding!

Carnegie Mellon University

–– If user has time to scan results exhaustively, retrieval is If user has time to scan results exhaustively, retrieval is a matter of watching, selecting, and sorting a matter of watching, selecting, and sorting quicklyquickly

ForkBrowserForkBrowser

de Rooij, CIVR 2008

CrowdsourcingCrowdsourcing via timelinevia timeline

Snoek et al. ACM MM 2010

•• Poster by Poster by BaukeBauke FreiburgFreiburg–– Tuesday, Tuesday, 12:30 PM 12:30 PM -- 2:00 PM 2:00 PM

Multimedia event browsersMultimedia event browsers

•• ACM Multimedia 2012?ACM Multimedia 2012?

The future of video retrieval?The future of video retrieval?

Jonathan Wang, Carnegie Mellon University


29‐11‐2011


Interface gap best addressed byInterface gap best addressed by

•• TRECVID interactive search taskTRECVID interactive search task–– Interactively solve 20+ search topics Interactively solve 20+ search topics (10/15 minutes)(10/15 minutes)

R t 1 000 k d h tR t 1 000 k d h t b d lt t ib d lt t i–– Return 1,000 ranked shotReturn 1,000 ranked shot--based results per topicbased results per topic

–– Evaluate using Average PrecisionEvaluate using Average Precision

•• VideOlympicsVideOlympics showcaseshowcase

Video browsing at TRECVIDVideo browsing at TRECVID

•• Wide performance variationWide performance variation–– # concept detectors# concept detectors

2003

2004

–– Search interfaceSearch interface

–– Expert Expert vsvs novice user novice user

•• Most pronouncedMost pronounced–– 2003: 2003: InformediaInformedia classicclassic

–– 2005: Large semantic lexicon2005: Large semantic lexicon

–– 2008: Online learning2008: Online learning

2005

2006

2008

2009

2007

UvAUvA--MediaMillMediaMill@TRECVID@TRECVID

CrossBrowser

Snoek et al. TRECVID 04-09

• 200+ other interactive systemsTraditional systems

CriticismCriticism

•• Retrieval performance cannot be the only Retrieval performance cannot be the only evaluation criterionevaluation criterion–– Quality of detectors countsQuality of detectors counts–– Quality of detectors countsQuality of detectors counts

–– Experience of searcher countsExperience of searcher counts

–– Visualization of interface countsVisualization of interface counts

–– Ease of use countsEase of use counts

–– ……

Video browsing at Video browsing at VideOlympicsVideOlympics

•• Promote multiple facets of video searchPromote multiple facets of video search–– RealReal--time interactive video search ‘competition’time interactive video search ‘competition’

Si lt f lti l id h iSi lt f lti l id h i–– Simultaneous exposure of multiple video search enginesSimultaneous exposure of multiple video search engines

–– Highlight possibilities and limitations of stateHighlight possibilities and limitations of state--ofof--thethe--artart

ParticipantsParticipants


29‐11‐2011


Video trailerVideo trailer

http://www.VideOlympics.org

Conclusion on video browsingConclusion on video browsing

•• Interaction by browsing indispensible for any Interaction by browsing indispensible for any practical video search enginepractical video search engine

•• System should support user by learning and System should support user by learning and intuitive (mobile) intuitive (mobile) visualizationsvisualizations

Internet Video SearchInternet Video SearchConclusion on:Conclusion on:

conceptdetection

tellingstories

browsingvideovideo

videomeasuringfeatures

lexiconlearning


•• Content Based Multimedia Retrieval: Lessons Content Based Multimedia Retrieval: Lessons Learned from Two Decades of Research Learned from Two Decades of Research

ShihShih Fu Chang Columbia UniversityFu Chang Columbia University–– ShihShih--Fu Chang, Columbia University Fu Chang, Columbia University

•• SIGMM Technical Achievement SIGMM Technical Achievement AwardAward–– Tomorrow: Tomorrow: 10:15 10:15 AMAM--12:30 12:30 PM PM


•• Recommended special issues Recommended special issues –– IEEE Transactions on Pattern Analysis and Machine IEEE Transactions on Pattern Analysis and Machine

Intelligence, 30(11), November 2008Intelligence, 30(11), November 2008–– Proceedings of the IEEE, 96(4), April 2008Proceedings of the IEEE, 96(4), April 2008–– IEEE Transactions on Multimedia, 9(5), August 2007IEEE Transactions on Multimedia, 9(5), August 2007

•• 300 references on video search300 references on video search–– Snoek and Snoek and WorringWorring, Concept, Concept--Based Video Retrieval, Based Video Retrieval,

Foundations and Trends in Information Retrieval, Foundations and Trends in Information Retrieval, Vol. 2: No 4, pp 215Vol. 2: No 4, pp 215--322, 322, 2009.2009.

General references IGeneral references IColor Invariance. Jan-Mark Geusebroek, R. van den Boomgaard, Arnold W. M. Smeulders, H. Geerts. IEEE Trans. Pattern Analysis and Machine Intelligence, Volume 23 (12), page 1338-1350, 2001.

Distinctive Image Features from Scale-Invariant Keypoints. D. G. Lowe. Int'l Journal of Computer Vision, vol. 60, pp. 91-110, 2004.

Large-Scale Concept Ontology for Multimedia. M. R. Naphade, J. R. Smith, J. g p gy p , ,Tesic, S.-F. Chang, W. Hsu, L. S. Kennedy, A. G. Hauptmann, and J. Curtis,. IEEE MultiMedia, vol. 13, pp. 86-91, 2006.

Efficient Visual Search for Objects in Videos. J. Sivic and A. Zisserman. Proceedings of the IEEE, vol. 96, pp. 548-566, 2008.

High Level Feature Detection from Video in TRECVid: A 5-year Retrospective of Achievements. A. F. Smeaton, P. Over, and W. Kraaij, In Multimedia Content Analysis, Theory and Applications, (A. Divakaran, ed.), Springer, 2008.

Visually Searching the Web for Content. J. R. Smith and S.-F. Chang. IEEE MultiMedia, vol. 4, pp. 12-20, 1997.

Content Based Image Retrieval at the End of the Early Years. Arnold W. M. Smeulders, Marcel Worring, S. Santini, A. Gupta, R. Jain. IEEE Trans. Pattern Analysis and Machine Intelligence, Volume 22 (12), page 1349-1380, 2000.


29‐11‐2011


General references IIGeneral references IIThe Challenge Problem for Automated Detection of 101 Semantic Concepts in Multimedia. Cees G. M. Snoek, Marcel Worring, Jan C. van Gemert, Jan-Mark Geusebroek, Arnold W. M. Smeulders. ACM Multimedia, page 421-430, 2006.

The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing. Cees G. M. Snoek, Marcel Worring, Jan-Mark Geusebroek, Dennis C. Koelma, Frank J. Seinstra, Arnold W. M. Smeulders. IEEE Trans. Pattern Analysis and Machine Intelligence, Volume 28 (10), page 1678-1689, 2006.y g , ( ), p g ,

A Learned Lexicon-Driven Paradigm for Interactive Video Retrieval. Cees G. M. Snoek, Marcel Worring, Dennis C. Koelma, Arnold W. M. Smeulders. IEEE Trans. Multimedia, Volume 9 (2), page 280-292, 2007.

Adding Semantics to Detectors for Video Retrieval. Cees G. M. Snoek, BoukeHuurnink, Laura Hollink, Maarten de Rijke, Guus Schreiber, Marcel Worring. IEEE Trans. Multimedia, Volume 9 (5), page 975-986, 2007.

The MediaMill TRECVID 2004-2011 Semantic Video Search Engine. Cees G. M. Snoek et al. Proceedings of the TRECVID Workshop, 2004-2011.

Visual-Concept Search Solved?. Cees G.M. Snoek and Arnold W.M. Smeulders. IEEE Computer, Volume 43 (6), page 76-78, 2010.

General references IIIGeneral references IIIConcept-Based Video Retrieval. Cees G. M. Snoek, Marcel Worring. Foundations and Trends in Information Retrieval, Vol. 4 (2), page 215-322, 2009.

Local Invariant Feature Detectors: A Survey. T. Tuytelaars and K. Mikolajczyk. Foundations and Trends in Computer Graphics and Vision, vol. 3, pp. 177-280, 2008.

Evaluating Color Descriptors for Object and Scene Recognition. Koen E. A. van de Sande, Theo Gevers, Cees G. M. Snoek. IEEE Trans. Pattern Analysis d hi lli (i ) 2010

http://www.science.uva.nl/research/publications/

and Machine Intelligence (in press), 2010.

Visual Word Ambiguity. Jan C. van Gemert, Cor J. Veenman, Arnold W. M. Smeulders, Jan-Mark Geusebroek. IEEE Trans. Pattern Analysis and Machine Intelligence (in press), 2009.

Real-Time Bag of Words, Approximately. Jasper R. R. Uijlings, Arnold W. M. Smeulders, R. J. H. Scha. ACM Int'l Conference on Image and Video Retrieval, 2009.

Lessons Learned from Building a Terabyte Digital Video Library. H. D. Wactlar, M. G. Christel, Y. Gong, and A. G. Hauptmann. IEEE Computer, vol. 32, pp. 66-73, 1999.

Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid. Int'l Journal of Computer Vision, vol. 73, pp. 213-238, 2007.

Contact infoContact info

•• Cees Snoek Cees Snoek http://www.CeesSnoek.infohttp://www.CeesSnoek.info

•• Arnold Arnold SmeuldersSmeuldershttp://staff.science.uva.nl/~smeulderhttp://staff.science.uva.nl/~smeulder

internet video search a brief history of television...grey value as well as opponent color sets....

Documents