In this paper, we propose an algorithm which detects boundaries of objects from a color image. The result of this method is a binary image where the boundary points are only represented. A lot of methods of edge detection from color images have been developed before. One of the most efficient ones is based on vectorial computations of the tristimuli R, G, B. But, in the case of complex color images, it is difficult to automatically determine a global threshold, in order to find the boundary pixels. For this reason, we suggest a local thresholding algorithm, using co-operating relaxation process to enhance edge probabilities. The labeling relaxation algorithm processes probabilities which are the result of a gradient application to the different features of the color images. So, this algorithm is able to detect edges from a slope of intensity, saturation or hue. The relaxation algorithm analyzes four classes of pixels. Three classes represent a gradient filter output of the three color image features, R, G, B. At each pixel, the higher the response of a feature value, the higher the probability of the pixel to belong to the class corresponding to this feature. The last label represents the no edge pixel, whose probability to belong to it is computed with the probabilities of the three other ones. For each pixel, the sum of the four probabilities must be equal to 1. The process is iterated as many times as the probability of each pixel to belong to the no edge class is near 0 or 1. We consider that a pixel with a low probability to belong to no edge class represents an edge pixel. The efficacy of the relaxation algorithm depends on the choice of the compatibility coefficients. We propose to compute these coefficients with the initial probabilities of the pixels to belong to the four classes, by the evaluation of the neighboring mutual information between two classes. The compatibility coefficients definition is based on the mutual information of the classes at neighboring points. The presented method has been successfully tested on complex color images and compared with the classic edge detection methods. We show that this local segmentation method is better than a global one.