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Automatic image annotation with relevance feedback and latent semantic analysis

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Published in 5th International Workshop on Adaptive Multimedia Retrieval, AMR 2007. Paris (France) - Jul 5-6 - Springer. 2007, p. 71-84
Collection Lecture Notes in Computer Science; 4918
Abstract The goal of this paper is to study the image-concept relationship as it pertains to image annotation. We demonstrate how automatic annotation of images can be implemented on partially annotated databases by learning image-concept relationships from positive examples via inter-query learning. Latent semantic analysis (LSA), a method originally designed for text retrieval, is applied to an image/session matrix where relevance feedback examples are collected from a large number of artificial queries (sessions). Singular value decomposition (SVD) is exploited during LSA to propagate image annotations using only relevance feedback information. We will show how SVD can be used to filter a noisy image/session matrix and reconstruct missing values.
Keywords Database ManagementComputer EngineeringComputer ApplicationsInformation Storage and RetrievalMultimedia Information SystemsInformation Systems Applications (incl. Internet)
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Research groups Computer Vision and Multimedia Laboratory
Viper group
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MORRISON, Donn Alexander, MARCHAND-MAILLET, Stéphane, BRUNO, Eric. Automatic image annotation with relevance feedback and latent semantic analysis. In: 5th International Workshop on Adaptive Multimedia Retrieval, AMR 2007. Paris (France). [s.l.] : Springer, 2007. p. 71-84. (Lecture Notes in Computer Science; 4918) https://archive-ouverte.unige.ch/unige:47784

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Deposited on : 2015-03-06

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