PLSA-based image auto-annotation: constraining the latent space

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PLSA-based image auto-annotation: constraining the latent space
We address the problem of unsupervised image auto-annotation with probabilistic latent space models. Unlike most previous works, which build latent space representations assuming equal relevance for the text and visual modalities, we propose a new way of modeling multi-modal co-occurrences, constraining the definition of the latent space to ensure its consistency in semantic terms (words), while retaining the ability to jointly model visual information. The concept is implemented by a linked pair of Probabilistic Latent Semantic Analysis (PLSA) models. On a 16000-image collection, we show with extensive experiments that our approach significantly outperforms previous joint models. Categories and Subject Descriptors H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing—Indexing methods General Terms Algorithms, Theory, Languages Keywords Automatic Annotation of Images, Semantic Indexing, PLSA
Florent Monay, Daniel Gatica-Perez
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where MM
Authors Florent Monay, Daniel Gatica-Perez
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