Automatic Window Design for Gray-Scale Image Processing Based on Entropy Minimization

This paper generalizes the technique described in [1] to gray-scale image processing applications. This method chooses a subset of variables W (i.e. pixels seen through a window) that maximizes the information observed in a set of training data by mean conditional entropy minimization. The task is formalized as a combinatorial optimization problem, where the search space is the powerset of the candidate variables and the measure to be minimized is the mean entropy of the estimated conditional probabilities. As a full exploration of the search space requires an enormous computational effort, some heuristics of the feature selection literature are applied. The introduced approach is mathematically sound and experimental results with texture recognition application show that it is also adequate to treat problems with gray-scale images.