Classifying segments in edge detection problems

Edge detection problems try to identify those pixels that represent the boundaries of the objects in an image. The process for getting a solution is usually organized in several steps, producing at the end a set of pixels that could be edges (candidates to be edges). These pixels are then classified based on some local evaluation method, taking into account the measurements obtained in each pixel. In this paper, we propose a global evaluation method based on the idea of edge list to produce a solution. In particular, we propose an algorithm divided in four steps: in first place we build the edge list (that we have called segments); in second place we extract the characteristics associated to each segment (length, intensity, location,…); in the third step we learn which are the characteristics that make a segment good enough to be a boundary; finally, in the fourth place, we apply the classification task. In this work we have built the ground truth of edge list necessary for the supervised classification. Finally we test the effectiveness of this algorithm against other classical approaches.