A Rough Set Approach to Video Genre Classification

Video classification provides an efficient way to manage and utilize the video data. Existing works on this topic fall into this category: enlarging the feature set until the classification is reliable enough. However, some features may be redundant or irrelevant. In this paper, we address the problem of choosing efficient feature set in video genre classification to achieve acceptable classification results but relieve computation burden significantly. A rough set approach is proposed. In comparison with existing works and the decision tree method, experimental results verify the efficiency of the proposed approach.

[1]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[2]  Ning Zhong,et al.  Using Rough Sets with Heuristics for Feature Selection , 1999, Journal of Intelligent Information Systems.

[3]  Tsuhan Chen,et al.  Audio feature extraction and analysis for scene classification , 1997, Proceedings of First Signal Processing Society Workshop on Multimedia Signal Processing.

[4]  Xiaohua Hu.  Knowledge discovery in databases: an attribute-oriented rough set approach , 1996 .

[5]  HongJiang Zhang,et al.  Motion Pattern-Based Video Classification and Retrieval , 2003, EURASIP J. Adv. Signal Process..

[6]  Wolfgang Effelsberg,et al.  Automatic recognition of film genres , 1995, MULTIMEDIA '95.

[7]  Yongmin Li,et al.  Video classification using spatial-temporal features and PCA , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[8]  Ba Tu Truong,et al.  Automatic genre identification for content-based video categorization , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[9]  Zhu Liu,et al.  Classification TV programs based on audio information using hidden Markov model , 1998, 1998 IEEE Second Workshop on Multimedia Signal Processing (Cat. No.98EX175).

[10]  M. Zhang,et al.  A rough sets based approach to feature selection , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[11]  Yong Man Ro,et al.  Video genre classification using multimodal features , 2003, IS&T/SPIE Electronic Imaging.

[12]  Mubarak Shah,et al.  Movie genre classification by exploiting audio-visual features of previews , 2002, Object recognition supported by user interaction for service robots.

[13]  Zdzisław Pawlak,et al.  Rough sets. Present state and the future , 1993 .