Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Association and Content-Based Retrieval
IEEE Transactions on Knowledge and Data Engineering
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Manifold Learning Based Cross-media Retrieval: A Solution to Media Object Complementary Nature
Journal of VLSI Signal Processing Systems
Cross-modal correlation learning for clustering on image-audio dataset
Proceedings of the 15th international conference on Multimedia
ClassView: hierarchical video shot classification, indexing, and accessing
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Mining Semantic Correlation of Heterogeneous Multimedia Data for Cross-Media Retrieval
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
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In this paper, we propose a cross-media retrieval method for heterogeneous multimedia data by which the query examples and the returned results can be of different modalities, e.g., to query images by an example of audio clip. Taking multimedia location and content information into consideration, an affinity propagation based clustering approach is proposed to analyse and fuse the information carried by the co-existing multimedia objects so as to learn the semantic correlations among the heterogeneous multimedia data and perform cross-media retrieval. We also propose active learning methods of Relevance Feedback to make the search model more accurate.