Melodic matching techniques for large music databases
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Modern Information Retrieval
Implementation of relevance feedback for content-based music retrieval based on user prefences
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Personalization of user profiles for content-based music retrieval based on relevance feedback
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Learning in Region-Based Image Retrieval with Generalized Support Vector Machines
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 9 - Volume 09
Communications of the ACM - Music information retrieval
Adaptive Algorithms for Interactive Multimedia
IEEE MultiMedia
An Approximate String Matching Algorithm for Content-Based Music Data Retrieval
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Discovering nontrivial repeating patterns in music data
IEEE Transactions on Multimedia
An efficient and effective region-based image retrieval framework
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Relevance feedback in region-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Effective content-based music retrieval with pattern-based relevance feedback
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
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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.