Integrating spatial and color information in images using a statistical framework

Color histograms have been widely used successfully in many computer vision and image processing applications. However, they do not include any spatial information. In this paper, we propose a statistical model to integrate both color and spatial information. Our model is based on finite multiple-Bernoulli mixtures. For the estimation of the model’s parameters, we use a maximum a posteriori (MAP) approach through deterministic annealing expectation maximization (DAEM). Smoothing priors on the components parameters are introduced to stabilize the estimation. The selection of the number of clusters is based on stochastic complexity. The results show that our model achieves good performance in some image classification problems.