Neutrosophic Set Transformation Matrix Factorization Based Active Contours for Color Texture Segmentation

Abstract

Active contour model (ACM) has widely used for segmenting two-phase images. However, its performance may not be satisfactory for some color texture images when their features cannot be effectively extracted. To alleviate this problem, in this paper, a novel neutrosophic set transformation matrix factorization-based active contour (NSTMF-AC) approach is proposed for color texture segmentation. The proposed NSTMF-AC is an effective and robust color texture segmentation method. Particularly, to effectively capture a wide range of texture information, the proposed method extracts the features from the triple domains, including spatial, wavelet, and spectral domains, and then uses neutrosophic set (NS) transform and the corresponding operations to reduce the indeterminacy contained in the image. Furthermore, the method obtains the resulting NS transformation matrix and utilizes a factorization-based ACM to perform image segmentation. Finally, the proposed method is compared with the state-of-the-art segmentation algorithms on a variety of natural images. The experimental results demonstrate that the proposed NSTMF-AC method is more robust for two-phase image segmentation than other methods.