Watershed merging method for color images

Abstract

Watershed transformation can be applied to color as well as to gray-scale images. A problem arises when dealing with color images. It is caused by the fact that pixels in such images are vectors that describe all color components whereas the watershed transformation requires a scalar height function as its input. There are multiple gradient magnitude definitions for color images that allow for the needed conversion. As in the case of gray-scale images, the image after watershed transformation is heavily over-segmented. One can blur the image before calculating the gradient magnitude, threshold the gradient image or merge the resulting watersheds. Unfortunately, the result is still over-segmented.A solution presented in this paper complements those mentioned above. It uses hierarchical cluster analysis methods for joining similar classes of the over-segmented image into a given number of clusters. After the image has been preprocessed and segmented, the over-segmentation is reduced by means of the cluster analysis. The attribute values for each watershed in each color component are calculated and clustering is performed. The resulting similarity hierarchy allows for the simple selection of the number of clusters in the final segmentation.Several clustering methods, including complete linkage and Ward's methods with different sets of components, have been tested. Selected results are presented.