Multi-threshold Segmentation with Exponential Entropy Based on Co-Occurrence Matrix and Intelligent Optimization Algorithm

The co-occurrence matrix is a way to describe the spatially relevant information of an image. The image thresholding method based on the co-occurrence matrix has its advantages over the thresholding method based on 2D grey-scale histogram. The traditional 2D grey-scale histogram requires two thresholds for binary segmentation, whereas the grey-scale co-occurrence matrix requires only one threshold for binary segmentation. When performing multi-threshold segmentation of an image, using co-occurrence matrix reduces the number of thresholds by half compared to using two-dimensional grey-scale histogram. In this paper, exponential entropy is used to implement co-occurrence matrix threshold selection, and swarm intelligent optimization algorithms is used to reduce the computational effort of multi-threshold selection. This method is compared with the 2D histogram-based method and the traditional log-entropy-based threshold segmentation method on BSD500 images, and the metrics of PSNR, FSIM, and SSIM are analysed in conjunction with the three intelligent optimization algorithms, and the methods in this paper are all superior to other comparative methods.