BUPT-MCPRL at TRECVID 2009 *
Zhicheng Zhao, Yanyun Zhao, Zan Gao, Yuanbo Chen, Hua Yan, Wen Wang, Cheng Liu, Siyuan Wu, Han Zhang, Lingxi Wang, Yuanhui Mao, Anni Cai, Menghua Zhai · 2009
This paper describes BUPT-MCPRL systems for TRECVID 2009. We performed experiments in automatic search, HLF extraction, copy detection and event detection tasks. A. Automatic search A semantic-based video search system was proposed and brief description of submitted 10 runs is shown in Table.1. Table 1 The performance of 10 runs for automatic search Run ID infMAP Description F_A_N_BUPT-MCPR1 0.104 HLF-based retrieval and positive WDSS method F_A_N_BUPT-MCPR2 0.070 Concept-based retrieval and positive WDSS method F_A_N_BUPT-MCPR3 0.059 Concept-based retrieval and positive and negative WDSS method F_A_N_BUPT-MCPR4 0.131 Combining concept lexicons of MCPR1 High-Level-Features and MCPR2 search topics and using positive WDSS method F_A_N_BUPT-MCPR5 0.032 Concept-based retrieval with example bagging method F_A_N_BUPT-MCPR6 0.024 Visual example-based retrieval F_A_N_BUPT-MCPR7 0.024 Concept-based retrieval with example weighting method F_A_N_BUPT-MCPR8 0.016 Re-rank MCPR7 with face score F_A_N_BUPT-MCPR9 0.009 Fusion MCPR6 and MCPR 7 and re-rank with face score F_A_N_BUPT-MCPR10 0.048 Fusion with MCPR5, MCPR 6 and MCPR 7 B. High-level feature extraction In this year, focus of our HLF system was on boosting and fusion of low-level features, the difference of classifiers with cross-validation, and re-ranking of results according to face detection. HLF Run infMAP Description Table 2 HLF results and description of BUPT-MCPRL system BUPT-MCPRL_Sys1 0.0313 BUPT-MCPRL_Sys3 is modified by face results.