Discovery and fusion of salient multi-modal features towards news story segmentation

Winston Hsu, Shih Fu Chang, Chih Wei Huang, Lyndon Kennedy, Ching Yung Lin, Giridharan Iyengar

研究成果: 雜誌貢獻會議論文同行評審

49 引文 斯高帕斯(Scopus)


In this paper, we present our new results in news video story segmentation and classification in the context of TRECVID video retrieval benchmarking event 2003. We applied and extended the Maximum Entropy statistical model to effectively fuse diverse features from multiple levels and modalities, including visual, audio, and text. We have included various features such as motion, face, music/speech types, prosody, and high-level text segmentation information. The statistical fusion model is used to automatically discover relevant features contributing to the detection of story boundaries. One novel aspect of our method is the use of a feature wrapper to address different types of features - asynchronous, discrete, continuous and delta ones. We also developed several novel features related to prosody. Using the large news video set from the TRECVID 2003 benchmark, we demonstrate satisfactory performance (F1 measures up to 0.76 in ABC news and 0.73 in CNN news), present how these multi-level multi-modal features construct the probabilistic framework, and more importantly observe an interesting opportunity for further improvement.

頁(從 - 到)244-258
期刊Proceedings of SPIE - The International Society for Optical Engineering
出版狀態已出版 - 2004
事件Storage and Retrieval Methods and Applications for Multimedia 2004 - San Jose, CA, United States
持續時間: 20 1月 200422 1月 2004


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