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PKU-IDM @ TRECVid 2010: Copy Detection with Visual-Audio Feature Fusion and Sequential Pyramid Matching * Abstract
| Content Provider | CiteSeerX |
|---|---|
| Author | Gao, Wen Mou, Luntian Tian, Yonghong Huang, Tiejun Wang, Yaowei Su, Chi Qian, Mengren Jiang, Menglin Fang, Xiaoyu Li, Yuanning |
| Abstract | Content-based copy detection (CBCD) over large corpus with complex transformations is important but challenging for video content analysis. To accomplish the TRECVid 2010 CBCD task, we’ve proposed a copy detection approach which exploits complementary visual/audio features and sequential pyramid matching (SPM). Several independent detectors first match visual key frames or audio clips using individual features, and then aggregate the frame level results into video level results with SPM, which works by partitioning videos into increasingly finer segments and calculating video similarities at multiple granularities. Finally, detection results from basic detectors are fused and further filtered to generate the final result. We have submitted four runs (i.e., “PKU-IDM.m.balanced.kraken”, “PKU-IDM.m.nofa.kraken”, “PKU-IDM.m.balanced.perseus ” and “PKU-IDM.m.nofa.perseus”) and achieved excellent NDCR performance along with competitive F1 measures. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Basic Detector Multiple Granularity Detection Result Video Content Analysis Complementary Visual Audio Feature Audio Clip Individual Feature Frame Level Result Final Result Video Level Result Cbcd Task Competitive F1 Measure Excellent Ndcr Performance Video Similarity Finer Segment Sequential Pyramid Matching Visual-audio Feature Fusion Large Corpus Pku-idm Trecvid Copy Detection Content-based Copy Detection Copy Detection Approach Several Independent Detector Complex Transformation Visual Key Frame Sequential Pyramid Matching Abstract |
| Content Type | Text |
