An improved thermal exchange optimization based GLCM for multi-level image segmentation

Abstract

The gray-level co-occurrence matrix (GLCM) can obtain the pixel matrix of the image, and selecting multiple thresholds for the matrix can obtain better segmentation results. However, as the number of threshold increases, the computational complexity of the algorithm will also increase. In order to solve this problem, this paper proposes a multi-threshold image segmentation method based on thermal exchange optimization (TEO) algorithm, and take a novel diagonal class entropy (DCE) as the fitness function. We improve TEO algorithm by using two strategic methods of Levy flight (LF) and opposition-based learning (OBL). In order to verify the segmentation ability of the proposed algorithm, color natural images, satellite images and Berkeley images are taken as experimental objects to analyze the segmentation result graph and image segmentation quality evaluation indexes. Experimental results show that the GLCM-ITEO algorithm has good segmentation capability, less CPU time.