1. Reporters: R98922004 Yun-Nung Chen, R98922033 Yu-Cheng Liu

2.  Ming Actor Correlations with Hierarchical Concurrence Parsing (ICASSP 2010)  Kun Yuan, Hongxun Yao, Rongrong Ji, Xiaoshuai Sun  Computer Science & Technology, Harbin Institute of Technology

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

4.  Actor correlations graph interfaces

6.  Top 20 shots

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

10.  2D-PCA reduces dimension  Features of same person may distribute discretely in feature space  Given 2 face sets F k and F l, 2 pose sets  If distance < T, 2 face sets belong to the same person

13.  A shot and its surrounding shots may present a plot between two actors in video  Gaussian weight measurement

15.  Scene level correlation  Video level correlation

17.  Construct correlations graph from

18.  A single character i  Character correlations between i and j

19.  Given i, j, sort RankScore (k) for all k  Show top 20 shots

20.  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

22.  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.  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%