A New Image Edge Detection Method using Quality-based Clustering

Due to the various limitations of existing edge detection methods, finding better algorithms for edge detection is still an active area of research. Many edge detection approaches have been proposed in the literature but in most cases, the basic approach is to search for abrupt change in color, intensity or other properties. Unfortunately, in many cases, images are corrupted with different types of noise which might cause sharp changes in some of these properties. In this paper, we propose a new method for edge detection which uses k-means clustering, and where different properties of image pixels were used as features. We analyze the quality of the different clusterings obtained using different k values (i.e., the predefined number of clusters) in order to choose the best number of clusters. The advantage of this approach is that it shows higher noise resistance compared to existing approaches. The performance of our method is compared against those of other methods by using images corrupted with various levels of “salt and pepper” and Gaussian noise. It is observed that the proposed method displayed superior noise resilience.