Content-Based Video Indexing and Retrieval
IEEE MultiMedia
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Content-based music structure analysis with applications to music semantics understanding
Proceedings of the 12th annual ACM international conference on Multimedia
A bootstrapping framework for annotating and retrieving WWW images
Proceedings of the 12th annual ACM international conference on Multimedia
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
Graph based multi-modality learning
Proceedings of the 13th annual ACM international conference on Multimedia
Understanding multimedia document semantics for cross-media retrieval
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
ClassView: hierarchical video shot classification, indexing, and accessing
IEEE Transactions on Multimedia
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
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Content-based cross-media retrieval is a new category of retrieval methods by which the modality of query examples and the returned results need not to be the same, for example, users may query images by an example of audio and vice versa. Multimedia Document (MMD) is a set of media objects that are of different modalities but carry the same semantics. In this paper, a graph based approach is proposed to achieve the content-based cross-media retrieval and MMD retrieval. Positive and negative examples of relevance feedback are used differently to boost the retrieval performance and experiments show that the proposed methods are very effective.