Learning concepts from visual scenes using a binary probabilistic model

This paper analyzes the use of visual words, as low-level image features, for learning and categorizing images. We show that this problem can be reduced to a simultaneous weighting of appropriate features and detection of clusters in a binary feature space. A probabilistic model is then proposed to quantify the effectiveness of visual words when treated as binary features. In order to learn the model, we consider a maximum a posteriori (MAP) approach. Experimental results are presented to illustrate the feasibility and merits of our approach.