Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Cross-modal correlation learning for clustering on image-audio dataset
Proceedings of the 15th international conference on Multimedia
Mining Semantic Correlation of Heterogeneous Multimedia Data for Cross-Media Retrieval
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Ranking with local regression and global alignment for cross media retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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Because it is very common that the heterogeneous multimedia data of the same semantics always exist jointly in many domain and application specific databases, it is very helpful to consider the location information when analyzing multimedia data. In this paper we propose a method of integrating the content and location context for multimedia data mining to enable the cross-media retrieval, by which the query examples and the returned results can be of different modalities, e.g. to query audios by an example of image. We construct a graph model by combing the multimedia content and location information. The graph model is then refined according to different strategies. The semantic correlations among multimedia data are calculated by learning the high-order neighborhood structure of the graph and the Multimedia Correlation Space is constructed in which the cross-media retrieval can be performed. We also propose different methods of Relevance Feedback to improve the search results. Experiments demonstrate the promise of the proposed method.