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Mining Actor Correlations with Hierarchical Concurrence Pasring

Reporters: R98922004 Yun-Nung Chen, R98922033 Yu-Cheng Liu. Mining Actor Correlations with Hierarchical Concurrence Pasring. Reference. Ming Actor Correlations with Hierarchical Concurrence Parsing (ICASSP 2010)

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Mining Actor Correlations with Hierarchical Concurrence Pasring

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  1. Reporters: R98922004 Yun-Nung Chen, R98922033 Yu-Cheng Liu Mining Actor Correlations with Hierarchical Concurrence Pasring

  2. Reference • Ming Actor Correlations with Hierarchical Concurrence Parsing (ICASSP 2010) • Kun Yuan, Hongxun Yao, RongrongJi, Xiaoshuai Sun • Computer Science & Technology, Harbin Institute of Technology

  3. Outline • Introduction • Actor Indexing • Mining Actor Correlations • Context-Based Actor Concurrence Graph • Ranking Concurrent Shots • Actor Correlation Changes Analysis • Experimental Results

  4. Introduction • Actor correlations graph interfaces

  5. Introduction • Actor correlations graph interfaces

  6. Introduction • Actor correlations graph interfaces • Top 20 shots

  7. Overview

  8. Actor Indexing

  9. Locating Actors • Shot boundary detection (SBD) • Shots and scenes • Locating actor faces & face tracking algorithm • Face set: different poses from the same actor

  10. Clustering Actors • 2D-PCA reduces dimension • Features of same person may distribute discretely in feature space • Given 2 face sets Fk and Fl, 2 pose sets • If distance < T, 2 face sets belong to the same person

  11. Clustering Actors

  12. Mining Actor Correlations

  13. Context-Based Actor Concurrence • A shot and its surrounding shots may present a plot between two actors in video • Gaussian weight measurement

  14. Context-Based Actor Concurrence

  15. Hierarchical Concurrence • Scene level correlation • Video level correlation

  16. Hierarchical Concurrence

  17. All Actor correlations graph • Construct correlations graph from

  18. Ranking Concurrent Shots • A single character i • Character correlations between i and j

  19. Ranking Concurrent Shots • Given i, j, sort RankScore<i, j>(k) for all k • Show top 20 shots

  20. Actor Correlation Changes Analysis • Two actors’ correlation changes with story • Analyze the difference of concurrence • R(i, j)A correlation measure between i and j in the part A • Change ratio Hlp

  21. Experimental Results

  22. Experiment Setup • 20 hours video of “Friends” TV series • About 4000 shots • Over 800 face sets • Clustering into about 60 face sets (T = 0.25) • Manual labeling to 17 actors

  23. Evaluation • The actor concurrence precision in all ranking shots is up to 90% • The precision of each two actor’s co-occurrence in ranking top 20 is up to 98%

  24. Thanks for your attention. 

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