Color image segmentation based on hybridization between Canny and k-means

Image segmentation is an important step in any image analysis process. In the literature, there are two dual approaches. The contour segmentation approach consists in locating the object boundaries and the segmentation approach by region consists in partitioning the image into a set of regions. The best results of segmentation are obtained by cooperating these two approaches. They are more effective because the disadvantages of one method can be overcome by the advantages of another method. In an image processing system, the most important operation is image segmentation. To date, there is no universal method of image segmentation. Any technique is only effective for a given type of image, for a given type of application, and in a given computer context. Because of these constraints, the various image processing strategies that have been proposed have asserted their inadequacies and limitations. It is therefore perfectly normal to explore new horizons and find new methods that are more flexible and more effective. In this paper, we will propose and discuss a cooperative approach between a k-means unsupervised classification method and Canny's contour detection method to improve color image segmentation results. First, we will apply the contour segmentation process by Canny's algorithm, and then we integrate the results obtained by another unsupervised classification segmentation process, which is k-means. Experimental results show the strengths of our approach in terms of accuracy, simplicity and efficiency.