logo Idiap Research Institute        
 [BibTeX] [Marc21]
On Automatic Annotation of Images with Latent Space Models
Type of publication: Idiap-RR
Citation: monay01
Number: Idiap-RR-31-2003
Year: 2003
Institution: IDIAP
Note: Published in ``Proc. ACM Multimedia 2003'', 2003
Abstract: Image auto-annotation, i.e., the association of words to whole images, has attracted considerable attention. In particular, unsupervised, probabilistic latent variable models of text and image features have shown encouraging results, but their performance with respect to other approaches remains unknown. In this paper, we apply and compare two simple latent space models commonly used in text analysis, namely Latent Semantic Analysis (LSA) and Probabilistic LSA (PLSA). Annotation strategies for each model are discussed. Remarkably, we found that, on a 8000-image dataset, a classic LSA model defined on keywords and a very basic image representation performed as well as much more complex, state-of-the-art methods. Furthermore, non-probabilistic methods (LSA and direct image matching) outperformed PLSA on the same dataset.
Userfields: ipdmembership={vision},
Projects Idiap
Authors Monay, Florent
Gatica-Perez, Daniel
Crossref by monayACM:2003
Added by: [UNK]
Total mark: 0
  • rr03-31.pdf
  • rr03-31.ps.gz