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 or 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 feature,. 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.