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學術出版;;Publications

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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Chen, Hsuan-ShengSubject: search.filters.subject.Multimedia system

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    Incorporating frequent pattern analysis into multimodal HMM event classification for baseball videos
    (2016-05)
    Chen, Hsuan-Sheng
    ;
    Tsai, Wen-Jiin
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Data mining and frequent pattern analysis have recently become a popular way of discovering new knowledge from a data set. However, it is rarely applied to video semantic analysis. Therefore, this paper introduces two methods: frequent-pattern trained HMM and frequent-pattern tailored HMM to incorporate frequent pattern analysis into multimodal HMM event classification for baseball videos. Besides, different symbol coding methods including temporal sequence coding and co-occurrence symbol coding for multimodal HMM classification are compared. The results of our experiments on baseball video event classification demonstrate that integration of frequent pattern analysis could help to improve event classification performances.
  • Thumbnail Image
    Item type:Item,
    A framework for video event classification by modeling temporal context of multimodal features using HMM
    (2014-02-01)
    Chen, Hsuan-Sheng
    ;
    Tsai, Wen-Jiin
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Semantic high-level event recognition of videos is one of most interesting issues for multimedia searching and indexing. Since low-level features are semantically distinct from high-level events, a hierarchical video analysis framework is needed, i.e., using mid-level features to provide clear linkages between low-level audio-visual features and high-level semantics. Therefore, this paper presents a framework for video event classification using temporal context of mid-level interval-based multimodal features. In the framework, a co-occurrence symbol transformation method is proposed to explore full temporal relations among multiple modalities in probabilistic HMM event classification. The results of our experiments on baseball video event classification demonstrate the superiority of the proposed approach. (C) 2013 Elsevier Inc. All rights reserved.