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 [BibTeX] [Marc21]
PLSA-based Image Auto-Annotation: Constraining the Latent Space
Type of publication: Conference paper
Citation: monayACM:2004
Booktitle: Proc. ACM Int. Conf. on Multimedia (ACM MM)
Year: 2004
Note: IDIAP-RR 04-30
Crossref: monay02:
Abstract: 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 and using various performance measures, that our approach significantly outperforms previous joint models.
Userfields: ipdmembership={vision},
Projects Idiap
Authors Monay, Florent
Gatica-Perez, Daniel
Added by: [UNK]
Total mark: 0
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