WordNet: a lexical database for English
Communications of the ACM
Query type classification for web document retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
MARSYAS: a framework for audio analysis
Organised Sound
MARSYAS: a framework for audio analysis
Organised Sound
Learning query-class dependent weights in automatic video retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Automatic discovery of query-class-dependent models for multimodal search
Proceedings of the 13th annual ACM international conference on Multimedia
Probabilistic latent query analysis for combining multiple retrieval sources
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
QueST: querying music databases by acoustic and textual features
Proceedings of the 15th international conference on Multimedia
SVM optimization: inverse dependence on training set size
Proceedings of the 25th international conference on Machine learning
Introduction to Information Retrieval
Introduction to Information Retrieval
Boosting image retrieval through aggregating search results based on visual annotations
MM '08 Proceedings of the 16th ACM international conference on Multimedia
CompositeMap: a novel framework for music similarity measure
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Signal Processing
Multiple feature fusion for social media applications
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Large-scale music tag recommendation with explicit multiple attributes
Proceedings of the international conference on Multimedia
Probabilistic image tagging with tags expanded by text-based search
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Document dependent fusion in multimodal music retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Coached active learning for interactive video search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
A self-organizing map for transactional data and the related categorical domain
Applied Soft Computing
A survey of music similarity and recommendation from music context data
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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The combination of heterogeneous knowledge sources has been widely regarded as an effective approach to boost retrieval accuracy in many information retrieval domains. While various technologies have been recently developed for information retrieval, multimodal music search has not kept pace with the enormous growth of data on the Internet. In this paper, we study the problem of integrating multiple online information sources to conduct effective query dependent fusion (QDF) of multiple search experts for music retrieval. We have developed a novel framework to construct a knowledge space of users' information need from online folksonomy data. With this innovation, a large number of comprehensive queries can be automatically constructed to train a better generalized QDF system against unseen user queries. In addition, our framework models QDF problem by regression of the optimal combination strategy on a query. Distinguished from the previous approaches, the regression model of QDF (RQDF) offers superior modeling capability with less constraints and more efficient computation. To validate our approach, a large scale test collection has been collected from different online sources, such as Last.fm, Wikipedia, and YouTube. All test data will be released to the public for better research synergy in multimodal music search. Our performance study indicates that the accuracy, efficiency, and robustness of the multimodal music search can be improved significantly by the proposed folksonomy-RQDF approach. In addition, since no human involvement is required to collect training examples, our approach offers great feasibility and practicality in system development.