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 a bag of visual words vector directly, and training a multi-way classifier on these vectors. To this end we introduce a novel vocabulary using dense colour SIFT descriptors, and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learnt, and the type of discriminative classifier used (k-nearest neighbour or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases using the authors' own datasets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos.