Graph-Based Regularization of Binary Classifiers for Texture Segmentation
Résumé
In this paper, we propose to improve a recent texture-based graph regularization model used to perform image segmentation by including a binary classifier in the process. Built upon two non-local image processing techniques, the addition of a classifier brings to our model the ability to weight texture features according to their relevance. The graph regularization process is then applied on the initial segmentation provided by the classifier in order to clear it from most imperfections. Results are presented on artificial and medical images, and compared to an active contour driven by classifiers segmentation algorithm, highlighting the increased generality and accuracy of our model.