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 [BibTeX] [Marc21]
PLSA-based Image Auto-Annotation: Constraining the Latent Space
Type of publication: Idiap-RR
Citation: monay02
Number: Idiap-RR-30-2004
Year: 2004
Institution: IDIAP
Note: Published in ``Proc. ACM Multimedia 2004'', 2004
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},
Keywords:
Projects Idiap
Authors Monay, Florent
Gatica-Perez, Daniel
Crossref by monayACM:2004
Added by: [UNK]
Total mark: 0
Attachments
  • rr04-30.pdf
  • rr04-30.ps.gz
Notes