Joint Summarization of Large-scale Collections of Web Images and Videos for Storyline Reconstruction...

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Joint Summarization of Large-scale Collections of Web Images and Videos

for Storyline Reconstruction

Gunhee Kim Leonid Sigal Eric P. Xing

June 16, 2014

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• Problem Statement

• Algorithm Video summarization

Storyline reconstruction

• Experiments

• Conclusion

Outline

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Background

Online photo/video sharing becomes so popular

Information overload problem in visual data

Average 3,000 pictures uploaded per minute

100 hours of video are uploaded per minute

Any efficient and comprehensive summary?

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Our Objective

Jointly summarize large sets of online images and videos

• The characteristics of two media are complementary

A user video

Videos: Much redundant and noisy information

backlit subjectsfull of trivial BGoverexposure

A set of photo streams

Images: More carefully taken from canonical viewpoints

Video summarizationCollections of Images

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Our Objective

Jointly summarize large sets of online images and videos


A set of user videos

Images: Sequential structure is often missing

A photo stream

Videos: Motion pictures

Image summarization Collections of Videos

Problem Statement

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(Input) A set of photo streams and user videos for a topic of interest

• Edges: chronological or causal relations (i.e., recur in many photo streams)

• Vertices: dominant image clusters

(Output1) Video summary: keyframe-based summarization

(Output2) Image summary as Storyline graph

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Flickr and YouTube Dataset

20 outdoor recreational classes

SurfingBeach

HorseRiding

RAfting

YAcht

Air Ball-ooning

ROwing

ScubaDiving

FormulaOne

SNowboarding

SafariPark

MountainCamping

RockClimbing

Tour deFrance

LondonMarathon

FlyFishing

• # videos (15,912)

Independ-ence Day

ChineseNew year

MemorialDay

St.PatrickDay

Wimble-don

• # images/photo streams (2,769,504, 35,545)

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• Experiments

• Conclusion

Outline

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Algorithm for Video Summarization

1. For each video , find the K-nearest photo streams

• Extreme diversity even with the same keywords

• Use Naïve-Bayes Nearest Neighbor method

A user video


2. Build a similarity graph between video frames and images

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1. For each video , find the K-nearest photo streams

• Extreme diversity even with the same keywords


A user videos


2. Build a similarity graph between video frames and images

• k-th order Markov chain between frames

• Each image casts m similarity votes

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3. Solve the following optimization problem of diversity ranking

A user videos


• Choose the nodes to place heat source to maximize the temperature• Sources should be (i) densely connected nodes, (ii) distant one another.

Submodular

[Kim et al. ICCV 2011]

A simply greedy achieves a constant factor approximation

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Image summarization (Storyline reconstruction)

• Experiments

• Conclusion

Outline

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Definition of Storyline Graphs

A storyline graph• : the vertex set = the set of codewords (i.e. image clusters)

Edges should be Sparse and Time-varying [Song et al. 09, Kolar et al.10]

• Images are too many, and much of them are largely redundant

• : popular transitions recurring across many photo streams

Sparsity : only a small number of branching stories per node

• A few nonzero elements in

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Definition of Storyline Graphs

Edges should be Sparse and Time-varying [Song et al. 09, Kolar et al.10]

Time-varying: popular transitions change over time

timeline

t = 10AM t = 12PM t = 2PM

Cluster 10 25

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A storyline graph• : the vertex set = the set of codewords (i.e. image clusters)

• Images are too many, and much of them are largely redundant

• : popular transitions recurring across many photo streams

At 1PM

At 7PM

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Directed Tree Derived from Photo Stream

1. For each photo stream , find the K-nearest videos


2. k-th order Markov chain btw images in a photo stream

4. Additional links are connected based on one-to-one correspondences

3. Keyframe detection for each neighbor video

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Directed Tree Derived from Photo Stream

5. Replace the vee structure (impractical artifact) by two parallel edges

✗• and are followed by .

• Both and must occur in order for to appear.

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Inferring Photo Storyline Graphs (1/3)

Input: A set of photo streams

Output : A set of adjacency matrices for

Objective: Derive the likelihood of an observed set of photo streams with reasonable assumptions

(A1) All photo streams are taken independently

Likelihood of a single photo stream

(A2) k-th order Markovian assumption btw consecutive images in PS (ex. k=1)

(A3) The codewords of xli are conditional independent one another given xl

i-1

Transition model

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•

For transition model, use a linear dynamic model

where Gaussian noise

• 1st order Markovian assumption

• k-th order Markovian assumption

A transition from x to y is very unlikely!

where

Transition model



where

For transition model, use a linear dynamic model

where Gaussian noise

• 1st order Markovian assumption

• The transition model per dimension can be

The log likelihood

Transition model

d-th row

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Optimization (1/2)

• (A4) Graphs vary smoothly over time.

For each t , estimate At by maximizing the log-likelihood

Optimization

Data (i.e. images) Timeline

Gaussian Kernel weighting

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Optimization (2/2)

In summary, the graph inference is

Iteratively solve a weighted L1-regularized least square problem

• Trivially parallelizable (for each d)

• Linear-time algorithm (eg. Coordinate descent)

• Important in our problem (i.e. handling millions of images).

where

Sparsity

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• Experiments

• Conclusion

Outline

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Evaluation of Video Summarization via AMT

(OursV): our method with videos only. (OursIV): our method with videos and images(Unif): uniform sampling. (Spect),(Kmeans): Spectral clustering/Kmeans(RankT): Keyframe extraction methods using the rank-tracing technique

Groundtruths for video summarication via Amazon Mechanical Turk

• (1) For each of 100 test videos, each algorithm selects K keyframes

• (2) At least five turkers are asked to choose GT keyframes

• (3) Compare between GT keyframes and ones chosen by the algorithm

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Comparison of Video Summarization

air+ballooning fly+fishing

AMT

(OursIV)

(OursV)

(Kmean)

(Unif)

(Unif): cannot correctly handle different lengths of subshots

(OursIV): Get help from the voting by more carefully taken images

(Kmean): hard to know best K

(OursV): suffer from the limitations of using low-level features only

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Evaluation on Storyline Graphs via AMT

Main difficulty of quantitative evaluation

• No groudtruth available.

• For a human subject, images and too many and graphs are too big

Crowdsourcing-based evaluation via

Ex) fly+fishing

Which is

better?

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Evaluation on Storyline Graphs via AMT

1. Each algorithm creates storyline per topic.

2. Sample 100 important images as test images

3. Each algorithm predicts next most-likely image after the test image

4. A pairwise preference test

• Given the test image, which of A and B is more likely to come next?

✔ Our method

Baseline 2

• Get responses from at least 3 turkers per test image

A crowd of human subjects evaluate only a basic unit (i.e. important edges of storyline).

Test image A

B

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Quantitative of Storyline Graphs via AMT

Results of pairwise preference tests

• The numbers indicates the percentage of responses that our prediction is more likely to occur next.

(OursV): our method with videos only. (OursIV): our method with videos and imagesNET: Network-based topic models ([Kim et al. 2008]) HMM: Hidden Markov ModelsPage: PageRank based image retrieval (no structural info)

• At least the number should be higher than 50% to validate the superiority of our algorithm.

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Qualitative Evaluation on Storyline Graphs

Given a pair of images in a novel photo stream, predict 10 images that are likely to occur between them using its storyline graph

• (HMM) retrieves reasonably good but highly redundant images. No branching structure.

• (PageRank) retrieves high-quality images but no sequential structure.

GT

Ours

(HMM)

(PageRank)

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Qualitative Evaluation on Storyline Graphs

Given a pair of images in a novel photo stream, predict 10 images that are likely to occur between them using its storyline graph

GT

Ours

A downsized storyline graph

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• Experiments

• Conclusion

Outline

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Structural summary with branching narratives

• Global optimality, linear complexity, and easy parallelization

Joint summarization of Flickr images and YouTube videos

Inference algorithm for sparse time-varying directed graphs

Conclusion

Semantic summary even with simple feature similarity

• 2.7M Flickr images and 17K YouTube videos for 20 classes

Images: More carefully taken from canonical viewpoints


Videos: Motion pictures

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Thank you !

Joint Summarization of Large-scale Collections of Web Images and Videos for Storyline Reconstruction...

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