Query refinement for multimedia similarity retrieval in MARS
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
A practical query-by-humming system for a large music database
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Personalization of user profiles for content-based music retrieval based on relevance feedback
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Relevance feedback for category search in music retrieval based on semantic concept learning
Multimedia Tools and Applications
A Novel Music Retrieval System with Relevance Feedback
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns
IEEE Transactions on Knowledge and Data Engineering
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To retrieve the preferred music piece from a music database, contentbased music retrieval has been studied for several years. However, it is not easy to retrieve the desired music pieces within only one query process. It motivates us to propose a novel query refinement technique called PBRF (Pattern-based Relevance Feedback) to predict the user's preference on music via a series of feedbacks, which combines three kinds of query refinement strategies, namely QPM (Query Point Movement), QR (Query Reweighting) and QEX (Query Expansion). In this work, each music piece is transformed into a pattern string, and the related discriminability and representability of each pattern can be calculated then. According to the information of discriminability and representability calculated, the user's preference on music can be retrieved by matching patterns of music pieces in the music database with those of a query music piece. In addition, with considering the local-optimal problem, extensive and intensive search methods based on user's feedbacks are proposed to approximate the successful search. Through the integration of QPM, QR, QEX and switch-based search strategies, the user's intention can be captured more effectively. The experimental results reveal that our proposed approach performs better than existing methods in terms of effectiveness.