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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Narrowing the semantic gap - improved text-based web document retrieval using visual features
IEEE Transactions on Multimedia
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Combining visual features and text data for medical image retrieval using latent semantic kernels
Proceedings of the international conference on Multimedia information retrieval
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Multi modal semantic indexing for image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
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A new approach to cross-modal multimedia retrieval
Proceedings of the international conference on Multimedia
Two-probabilistic latent semantic model for image annotation and retrieval
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Multimodal information spaces for content-based image retrieval
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PhotoGeo: a photo digital library with spatial-temporal support and self-annotation
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Proceedings of the 20th ACM international conference on Multimedia
Proceedings of the 20th ACM international conference on Multimedia
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This paper studies the effect of Latent Semantic Analysis (LSA) on two different tasks: multimedia document retrieval (MDR) and automatic image annotation (AIA). The contributions of this paper are twofold. First, to the best of our knowledge, this work is the first study of the influence of LSA on the retrieval of a significant number of multimedia documents (i.e. collection of 20000 tourist images). Second, it shows how different image representations (region-based and keypoint-based) can be combined by LSA to improve automatic image annotation. The document collections used for these experiments are the Corel photo collection and ImageCLEF 2006 collection.