Color image segmentation based on Decision-Theoretic Rough Set model and Fuzzy C-Means algorithm

Abstract

This paper proposes an approach which combines the Decision Theoretic Rough Set model (DTRS) and Fuzzy C-Means(FCM) algorithm to perform color image segmentation. The FCM algorithm has the limitation that it requires the initialization of cluster centroids and the number of clusters. In this paper, the DTRS model is applied to color image segmentation for the purpose of clustering validity analysis which could overcome the defect of the FCM algorithm. Firstly, we adopt the Turbopixel algorithm to split the color image into many small regions called superpixels for presegmentation. Based on color image color histogram feature extraction we use Bhattacharyya coefficient to measure the similarity between superpixels, which is in preparation for clustering validity analysis. It is our focus that we will obtain cluster centroids and the number of clusters using FCM. Our approach is according to the hierarchical clustering validity analysis algorithm using DTRS model. Finally, the FCM algorithm is utilized to achieve the result of color image segmentation. Experimental results show that the DTRS-based preprocessing approach can obtain better segmentation results than other improved FCM approaches such as ant colony algorithm or histogram thresholding approach.