Color texture image segmentation by using neutrosophic approach and wavelet transform

In this thesis, we propose a new approach for image segmentation that is based on neutrosophic set (NS) and wavelet decompositions for color texture image segmentation. It is aimed at segmentation of natural scenes, in which the color and texture of each segment does not typically exhibit uniform statistical characteristics. The proposed approach combines color information with the low-level features of grayscale component of the texture on NS domain for efficient segmentation. The proposed approach transforms both each color channel of the input image and the low-level features of grayscale image into the NS domain independently which is described using three membership sets: T, I and F. The entropy in NS is defined and employed to evaluate the indeterminacy. Two operations, ?-mean and ß-enhancement operations are proposed to reduce the set indeterminacy. Finally, the proposed method is employed to perform image segmentation using a ?-means clustering algorithm. Experiments are conducted on a variety of images, and our results are compared with those new existing segmentation algorithm. The experimental results demonstrate that the proposed approach can segment the color texture images effectively.Key words: Neutrosophic set, color texture image segmentation, wavelet transform