Adaptive SLIC-Based Fuzzy Intensity Dissimilarity Thresholding for Color Image Segmentation

Abstract

In order to make full use of the color information of the image and improve the accuracy of color image segmentation, this paper proposes an adaptive SLIC-based fuzzy intensity dissimilarity thresholding for color image segmentation, which does not need gray conversion. Firstly, the proposed algorithm adaptively selects the number of super-pixels through the sum of image information and image complexity, and uses SLIC technology to extract image super-pixels; Then, the median value of each channel pixel in each super-pixel block is used as the super-pixel value to calculate the super-pixel intensity information, and the super-pixel intensity histogram is counted; Finally, an intensity dissimilarity function based on IT2FS is constructed to search the optimal threshold. On Berkeley images and Weizmann images, the proposed algorithm is compared with the five related algorithms. The experiments show that the proposed algorithm has achieved good results in terms of visual effects and evaluation indicators, which proves the effectiveness of the algorithm.