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}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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