Correlated PLSA for Image Clustering

Probabilistic Latent Semantic Analysis (PLSA) has become a popular topic model for image clustering. However, the traditional PLSA method considers each image (document) independently, which would often be conflict with the real occasion. In this paper, we presents an improved PLSA model, named Correlated Probabilistic Latent Semantic Analysis (C-PLSA). Different from PLSA, the topics of the given image are modeled by the images that are related to it. In our method, each image is represented by bag-of-visual-words. With this representation, we calculate the cosine similarity between each pair of images to capture their correlations. Then we use our C-PLSA model to generate K latent topics and Expectation Maximization (EM) algorithm is utilized for parameter estimation. Based on the latent topics, image clustering is carried out according to the estimated conditional probabilities. Extensive experiments are conducted on the publicly available database. The comparison results show that our approach is superior to the traditional PLSA for image clustering.