Combining geometrical and textured information to perform image classification
Résumé
In this paper, we propose a framework to carry out supervised classification of images containing both textured and non textured areas. Our approach is based on active contours. The classification corresponds to the minimum of a functional. Using a decomposition algorithm inspired by the recent work of Y. Meyer, we can get two channels from the original image to classify: one containing the geometrical information, and the other the texture. Using the logic framework by Chan and Sandberg, we can then combine the information from both channels in a user definable way. Thus, we design a classification algorithm in which the different classes are characterized both from geometrical and textured features. Since natural images are combinations of both textured and non textured patterns, this new approach enlarges the scope of possible applications for active contours-based classification algorithms.