An Optimal Global Method for Classification of Color Pixels

We propose in this article a color image segmentation technique based on an optimization of the Mean Shift method. It consists in classifying clusters of data points of a digital image, it does not require any preliminary designation of the number of classes and of their centers. The Mean Shift method applied to a digital color image creates a new image made up of aggregates of points belonging to a finished number of color classes. The complexity of this method based on a global approach of the image is in O(NxN), N being the number of pixels of the image. Our idea consists in applying a change of scale to the image to be segmented, to reduce the quantity of information and, in using a median filter to decrease the number of colors to minimize the complexity of the latter. The comparative study which we present shows that the optimization which we proposed gives better, reliable results than the classic use of the Mean Shift method.