CONF
monayACM:2003/IDIAP
On Image Auto-Annotation with Latent Space Models
Monay, Florent
Gatica-Perez, Daniel
EXTERNAL
https://publications.idiap.ch/attachments/papers/2003/monay-acm-sp054.pdf
PUBLIC
https://publications.idiap.ch/index.php/publications/showcite/monay01
Related documents
Proc. ACM Int. Conf. on Multimedia (ACM MM)
2003
IDIAP-RR 03-31
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.
REPORT
monay01/IDIAP
On Automatic Annotation of Images with Latent Space Models
Monay, Florent
Gatica-Perez, Daniel
EXTERNAL
https://publications.idiap.ch/attachments/reports/2003/rr03-31.pdf
PUBLIC
Idiap-RR-31-2003
2003
IDIAP
Published in ``Proc. ACM Multimedia 2003'', 2003
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.