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Unsupervised Commonality Discovery in Images

Unsupervised Temporal Commonality Discovery Wen-Sheng Chu , Feng Zhou and Fernando De la Torre Robotics Institute, Carnegie Mellon University July 9, 2013. Unsupervised Commonality Discovery in Images. (Chu’10, Mukherjee’11, Collins’12). Where are the repeated patterns?.

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Unsupervised Commonality Discovery in Images

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  1. Unsupervised Temporal Commonality DiscoveryWen-Sheng Chu, Feng Zhou and Fernando De la TorreRobotics Institute, Carnegie Mellon UniversityJuly 9, 2013

  2. Unsupervised Commonality Discoveryin Images (Chu’10, Mukherjee’11, Collins’12) Where are the repeated patterns?

  3. Unsupervised Commonality Discoveryin Videos? • We name it Temporal Commonality Discovery (TCD). • Goal: Given two videos, discover common events in an unsupervised fashion.

  4. TCD is hard! 1) No prior knowledge on commonalities • We do not know what, where and how many commonalities exist in the video 2) Exhaustive search are computationally prohibitive • E.g., two videos with 300 frames have >8,000,000,000 possible matches. possible locations possible lengths possibilities/sequence Another possibilities/sequence

  5. Formulation Integer programming!

  6. Optimization: Interpretation

  7. Optimization: Native Search Complexity

  8. Optimization: Branch-and-Bound • Similar to the idea of ESS (Lampert’08), we search the space by splittingintervals.

  9. Optimization: Branch-and-Bound • Bounding histogram bins

  10. Optimization: Branch-and-Bound • Bounding L1 distance: • Intersection similarity: • X2 distance:

  11. Searching Structure State S = (Rectangle set; score) (B1,E1,B2,E2; -105) (B1,E1,B2,E2; -10) (B1,E1,B2,E2; -50) Unlikelysearchregions … (B1,E1,B2,E2; 32) Priority queue (sorted by bound scores)

  12. Algorithm Pop out the top state Top state 2. Split (B1,E1,B2,E2; -105) (B1,E1,B2,E2; -105) (B1,E1,B2,E2; -50) … (B1,E1,B2,E2; 32) Priority queue (sorted by bound scores)

  13. Algorithm 4. Push back the split states Top state 3. Compute bounding scores (B1,E1,B2,E2; -105) (B1,E1,B2,E2; -50) (B1,E’1,B2,E2; -76) … (B1,E1,B2,E2; 32) Priority queue (sorted by bound scores) (B1,E’’1,B2,E2; -61)

  14. Algorithm • The algorithm stop when the top state contains an unique rectangle. (B1,E’1,B2,E2; -76) Top state (B1,E’’1,B2,E2; -61) (B1,E1,B2,E2; -50) Omit most of the search space with large distances … (B1,E1,B2,E2; 32) Priority queue (sorted by bound scores)

  15. Compare with Relevant Work • Difference between TCD and ESS [1]/STBB[2] • Different learning framework: • Unsupervisedv.s. Supervised • New bounding functions for TCD • Difference between TCD and [3] • Different objective: • Commonality Discoveryv.s. Temporal Clustering [1] “Efficient subwindowsearch: A branch and bound framework for object localization”, PAMI 2009. [2] “Discriminative video pattern search for efficient action detection”, PAMI 2011. [3] “Unsupervised discovery of facial events”, in CVPR 2010.

  16. Experiment (1): Synthesized Sequence Histograms of the discovered pair of subsequences

  17. Experiment (2): Discover Common Facial Actions • RU-FACS dataset* • Interview videos with 29 subjects • 5000~8000 frames/video • Collect 100 segments that containing smiley mouths (AU-12) • Evaluate in terms of averaged precision * “Automatic recognition of facial actions in spontaneous expressions”, Journal of Multimedia 2006.

  18. Experiment (2): Discover Common Facial Actions

  19. Experiment (2): Speed EvaluationSpeed #evaluation of the distance function • Parametric settings for Sliding Windows (SW) • Log of #evaluations: • Quality of discovered patterns: • a

  20. Experiment (2): Discover Common Facial Actions • Compare with LCCS* on -distance * “Frame-level temporal calibration of unsynchronized cameras by using Longest Consecutive Common Subsequence”, ICASSP 2009.

  21. Experiment (3): Discover Multiple Common Human Motions • CMU-Mocap dataset: • http://mocap.cs.cmu.edu/ • 15 sequences from Subject 86 • 1200~2600 frames and up to 10 actions/seq • Exclude the comparison with SW because it needs >1012 evaluations

  22. Experiment (3): Discover Multiple Common Human Motions

  23. Experiment (3): Discover Multiple Common Human Motions • Compare with LCCS* on -distance

  24. Extension: Video Indexing • Goal: Given a query , find the best common subsequence in the target video • A straightforward extension: TemporalSearch Space

  25. A Prototype for Video Indexing

  26. Summary • We have introduced: • NEW! ProblemAlgorithm Bounding functions • Able to discovery temporal commonality efficiently and effectively • Next: • Use submodular functions to improve speed • Explore more bounds for other metrics between temporal segments, such as dynamic time warping • More applications. E.g., video indexing, co-occurrence detection, irregularity detection, etc.

  27. Questions? [1] “Common Visual Pattern Discovery via Spatially Coherent Correspondences,” In CVPR 2010. [2] “MOMI-cosegmentation: simultaneous segmentation of multiple objects among multiple images,” In ACCV 2010. [3] “Scale invariant cosegmentation for image groups,” In CVPR 2011. [4] “Random walks based multi-image segmentation: Quasiconvexity results and GPU-based solutions,” In CVPR 2012. [5] “Frame-level temporal calibration of unsynchronized cameras by using Longest Consecutive Common Subsequence,” In ICASSP 2009. [6] “Efficient ESS with submodular score functions,” In CVPR 2011. http://humansensing.cs.cmu.edu/wschu/

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