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Abstract: Low-level features are now becoming insufficient to build efficient content-based retrieval systems. Users are not interested any longer in retrieving visually similar content, but they expect retrieval systems to also find documents with similar semantic content. Bridging the gap between low-level features and semantic content is a challenging task necessary for future retrieval systems. Latent Semantic Analysis (LSA) was successfully introduced to efficiently index text documents by... (Update)
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1.3: Improved Video Content Indexing by Multiple Latent.. - Souvannavong.. (2004) (Correct)
0.3: Using Structure for Video Object Retrieval - Hohl, Souvannavong, Merialdo.. (2004) (Correct)
0.2: Latent Semantic Analysis for an Effective Region-Based .. - Souvannavong.. (2004) (Correct)
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BibTeX entry: (Update)
@misc{ souvannavong-improved,
author = "Fabrice Souvannavong and Bernard Merialdo and Benoît Huet",
title = "Improved Video Content Indexing by Multiple Latent Semantic Analysis",
url = "citeseer.ist.psu.edu/757420.html" }
Citations (may not include all citations):568 Indexing by latent semantic analysis - Deerwester, Dumais et al. - 1990
123 Probabilistic latent semantic indexing - Hofmann - 1999
57 A fully automated content-based video search engine supporti.. - Chang, Chen et al. - 1998
15 a novel approach to video indexing and retrieval (context) - Naphade, Kristjansson et al. - 1998
10 Indexing audio documents by using latent semantic analysis a.. - Kurimo - 1999
10 From features to semantics: Some preliminary results - Zhao, Grosky - 2000
8 On image auto-annotation with latent space models - Monay, Gatica-Perez - 2003
7 Efficiently computing a good segmentation - Felzenszwalb, Huttenlocher - 1998
3 Video content modeling with latent semantic analysis (context) - Souvannavong, Merialdo et al. - 2003
2 Latent semantic indexing for video content modeling and anal.. (context) - Souvannavong, Merialdo et al. - 2003
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