Color texture image segmentation based on neutrosophic set and wavelet transformation

Efficient and effective image segmentation is an important task in computer vision and pattern recognition. Since fully automatic image segmentation is usually very hard for natural images, interactive schemes with a few simple user inputs are good solutions. In this paper, we propose a fully automatic new approach for color texture image segmentation based on neutrosophic set (NS) and multiresolution wavelet transformation. It aims to segment the natural scene images, in which the color and texture of each region does not have uniform statistical characteristics. The proposed approach combines color information with the texture information on NS and wavelet domain for segmentation. At first, it transforms each color channel and the texture information of the input image into the NS domain independently. The entropy is defined and employed to evaluate the indeterminacy of the image in NS domain. Two operations, α-mean and β-enhancement operations are proposed to reduce the indeterminacy. Finally, the proposed method is employed to perform image segmentation using a γ-K-means clustering. The determination of the cluster number K is carried out with cluster validity analysis. Two different segmentation evaluation criterions were used to determine the segmentations quality. Experiments are conducted on a variety of images, and the results are compared with those new existing segmentation algorithm. The experimental results demonstrate that the proposed approach can segment the color images automatically and effectively.

► A fully automatic approach for color texture image segmentation based on neutrosophic set and wavelet transformation.
► Adaptively selection on parameters in neutrosophic set.
► Cluster validity analysis to determination of the cluster.