Probabilistic Classification of Image Regions using an Observation-Constrained Generative Approach

Abstract

In generic image understanding applications, one of the goals is to interpret the semantic context of the scene (e.g., beach, office etc.). In this paper, we propose a probabilistic region classification scheme for natural scene images as a priming step for the problem of context interpretation. In conventional generative methods, a generative model is learnt for each class using all the available training data belonging to that class. However, if a set of newly observed data has been generated because of the subset of the model support, using the full model to assign generative probabilities can produce serious artifacts in the probability assignments. This problem arises mainly when the different classes have multimodal distributions with considerable overlap in the feature space. We propose an approach to constrain the class generative probability of a set of newly observed data by exploiting the distribution of the new data itself and using linear weighted mixing. A KL-Divergence-based fast model selection procedure is also proposed for learning mixture models in a sparse feature space. The preliminary results on the natural scene images support the effectiveness of the proposed approach.