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On image auto-annotation with latent space models
2003
Proceedings of the eleventh ACM international conference on Multimedia - MULTIMEDIA '03
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
doi:10.1145/957052.957070
fatcat:s3fskg7tl5bvpdjc5vpbyjrcny