Image categorization via robust pLSA

This paper presents a novel method to give a good initial estimate of the probabilistic latent semantic analysis (pLSA) model using rival penalized competitive learning (RPCL), since the expectation maximization (EM) algorithm used to train the model is sensitive to the initialization. As a generative model from the statistical text literature, pLSA is further applied to the bag-of-words representation for each image in the database. Especially for those images containing multiple object categories (e.g. grass, roads, and buildings), we aim to discover the objects (i.e., latent topics) in an unsupervised way using pLSA. Based on the discovered topics, image categorization is then carried out by ensemble-based support vector machine (SVM). We then find in the experiments that the pLSA model with RPCL initialization followed by ensemble-based SVM categorization is robust to the changes of the visual vocabulary and the number of latent topics.