Topic models for automatic image annotation

Abstract — Modern image retrieval systems, which allow users to use textual queries and perform content-based image retrieval (CBIR), depend greatly on Automatic Image Annotation. Many models namely unsupervised topic models have successfully been applied in text analysis and are showing encouraging results in automatic image annotation. In this work, we first describe the basic topic models: the latent semantic analysis (LSA), the probabilistic latent semantic analysis (PLSA) and the latent Dirichlet allocation (LDA). Since these models assume that documents are represented as “bag of words ” in text analysis, we then describe BOV-based image representation, an analogous representation adapted to image annotation. Based on SIFT technique followed by vectorial quantization, this representation allow image to be as “bag of visterms ” (BOV). Finally, we describe some advanced topic models: GM-PLSA, GM-LDA and CORR-LDA, which are used in image annotation. Keywords- automatic image annotation; topic models; CBIR; SIFT; BOV. I