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AMR
2007
Springer

Automatic Image Annotation with Relevance Feedback and Latent Semantic Analysis

9 years 3 months ago
Automatic Image Annotation with Relevance Feedback and Latent Semantic Analysis
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 imageconcept 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.
Donn Morrison, Stéphane Marchand-Maillet, E
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where AMR
Authors Donn Morrison, Stéphane Marchand-Maillet, Eric Bruno
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