Pre-CVPR Selective Transfer Machine for Facial AU Detection

Unsupervised Temporal Commonality Discovery

Wen-Sheng Chu, Feng Zhou and Fernando De la TorreRobotics Institute, Carnegie Mellon UniversityJuly 9, 20131

Unsupervised Commonality Discoveryin Images

Where are the repeated patterns?2

(Chu10, Mukherjee11, Collins12)Unsupervised Commonality Discoveryin Videos?We name it Temporal Commonality Discovery (TCD).Goal: Given two videos, discover common events in an unsupervised fashion.

3TCD is hard!1) No prior knowledge on commonalitiesWe do not know what, where and how many commonalities exist in the video2) Exhaustive search are computationally prohibitiveE.g., two videos with 300 frames have >8,000,000,000 possible matches.

possible locations possible lengths possibilities/sequence

Another possibilities/sequence

4Formulation

5Integer programming!Optimization: Interpretation

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Optimization: Native SearchComplexity

7Optimization: Branch-and-BoundSimilar to the idea of ESS (Lampert08), we search the space by splitting intervals.

8Optimization: Branch-and-BoundBounding histogram bins

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Bounding L1 distance:

Intersection similarity:

X2 distance:

Optimization: Branch-and-Bound

10Unlikelysearchregions(B1,E1,B2,E2; -10)Searching Structure

(B1,E1,B2,E2; 32)Priority queue(sorted by bound scores)(B1,E1,B2,E2; -50)(B1,E1,B2,E2; -105)

State S = (Rectangle set; score)11

(B1,E1,B2,E2; -105)Algorithm

(B1,E1,B2,E2; 32)Priority queue(sorted by bound scores)(B1,E1,B2,E2; -50)(B1,E1,B2,E2; -105)Top statePop out the top state

2. Split12(B1,E1,B2,E2; -105)Algorithm(B1,E1,B2,E2; 32)Priority queue(sorted by bound scores)(B1,E1,B2,E2; -50)Top state

(B1,E1,B2,E2; -76)

(B1,E1,B2,E2; -61)3. Compute bounding scores4. Push back thesplit states 13Algorithm(B1,E1,B2,E2; 32)Priority queue(sorted by bound scores)(B1,E1,B2,E2; -50)Top state(B1,E1,B2,E2; -76)(B1,E1,B2,E2; -61)The algorithm stop when the top state contains an unique rectangle.

Omit most of the search space withlarge distances14Compare with Relevant WorkDifference between TCD and ESS [1]/STBB[2]Different learning framework: Unsupervised v.s. Supervised New bounding functions for TCDDifference between TCD and [3]Different objective: Commonality Discovery v.s. Temporal Clustering

[1] Efficient subwindow search: 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 2010.15

Experiment (1): Synthesized Sequence

Histograms of the discovered pair of subsequences16Experiment (2): Discover Common Facial ActionsRU-FACS dataset*Interview videos with 29 subjects5000~8000 frames/videoCollect 100 segments that containing smiley mouths (AU-12)Evaluate in terms of averaged precision

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* Automatic recognition of facial actions in spontaneous expressions, Journal of Multimedia 2006.Experiment (2): Discover Common Facial Actions

18Parametric settings for Sliding Windows (SW)

Log of #evaluations:

Quality of discovered patterns:a

Experiment (2): Speed EvaluationSpeed #evaluation of the distance function

19Experiment (2): Discover Common Facial ActionsCompare with LCCS* on -distance20* Frame-level temporal calibration of unsynchronized cameras by using Longest Consecutive Common Subsequence, ICASSP 2009.

Experiment (3): Discover Multiple Common Human MotionsCMU-Mocap dataset:http://mocap.cs.cmu.edu/15 sequences from Subject 861200~2600 frames and up to 10 actions/seqExclude the comparison with SW because it needs >1012 evaluations

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Experiment (3): Discover Multiple Common Human Motions

22Experiment (3): Discover Multiple Common Human MotionsCompare with LCCS* on -distance23

Extension: Video IndexingGoal: Given a query , find the best common subsequence in the target videoA straightforward extension:

TemporalSearchSpace

24A Prototype for Video Indexing25

Summary26Questions?[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.

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http://humansensing.cs.cmu.edu/wschu/