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    政大機構典藏 > 理學院 > 資訊科學系 > 期刊論文 >  Item 140.119/29580


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    题名: Relevance feedback for category search in music retrieval based on semantic concept learning
    作者: 沈錳坤;Meng-Fen Chiang;Fang-Fei Kuo
    贡献者: 資訊科學系
    关键词: Category search;Music retrieval;Relevance feedback;Semantic concept learning
    日期: 2008-03
    上传时间: 2009-08-24 13:16:53 (UTC+8)
    摘要: Traditional content-based music retrieval systems retrieve a specific music object which is similar to what a user has requested. However, the need exists for the development of category search for the retrieval of a specific category of music objects which share a common semantic concept. The concept of category search in content-based music retrieval is subjective and dynamic. Therefore, this paper investigates a relevance feedback mechanism for category search of polyphonic symbolic music based on semantic concept learning. For the consideration of both global and local properties of music objects, a segment-based music object modeling approach is presented. Furthermore, in order to discover the user semantic concept in terms of discriminative features of discriminative segments, a concept learning mechanism based on data mining techniques is proposed to find the discriminative characteristics between relevant and irrelevant objects. Moreover, three strategies, the Most-Positive, the Most-Informative, and the Hybrid, to return music objects concerning user relevance judgments are investigated. Finally, comparative experiments are conducted to evaluate the effectiveness of the proposed relevance feedback mechanism. Experimental results show that, for a database of 215 polyphonic music objects, 60% average precision can be achieved through the use of the proposed relevance feedback mechanism.
    關聯: Multimedia Tools and Applications, 39(2), 243-262
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1007/s11042-008-0201-8
    DOI: 10.1007/s11042-008-0201-8
    显示于类别:[資訊科學系] 期刊論文

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