Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail we are given a set of labelled images of scenes (e.g. coast, forest, city, river, etc) and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent "topics" using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently training a multi-way classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by…