標題: Relevance feedback for category search in music retrieval based on semantic concept learning
作者: Shan, Man-Kwan
Chiang, Meng-Fen
Kuo, Fang-Fei
資訊工程學系
Department of Computer Science
關鍵字: category search;music retrieval;relevance feedback;semantic concept learning
公開日期: 1-Sep-2008
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.
URI: http://dx.doi.org/10.1007/s11042-008-0201-8
http://hdl.handle.net/11536/8400
ISSN: 1380-7501
DOI: 10.1007/s11042-008-0201-8
期刊: MULTIMEDIA TOOLS AND APPLICATIONS
Volume: 39
Issue: 2
起始頁: 243
結束頁: 262
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