An effective color image segmentation approach using superpixel-neutrosophic C-means clustering and gradient-structural similarity

Abstract

Due to the insufficient expression of uncertain information in fuzzy sets, the fuzzy C-means clustering algorithm is difficult to deal with clustered boundary and outliers. Therefore, this paper proposes a color image segmentation method based on neutrosophic C-means clustering. First, we improved the simple linear clustering algorithm to obtain accurate and natural adaptive local spatial neighborhoods. Secondly, local neighborhood information is added to the objective function of neutrosophic C-means clustering to obtain a more accurate membership. According to the membership, superpixels are divided into certainty group and uncertainty group. Finally, the certainty group is classified by the maximum membership, and the superpixels of the uncertain group are classified by the structural similarity. Experimental results show that the proposed method has better performance on clean and noise images.