Fruit Image Segmentation Using Teacher-Learner Optimization Algorithm and Fuzzy Entropy

Abstract

To perform robust fruit image segmentation is a challenging task due to several variances in the varieties. Thresholding is one of the popular segmentation methods in recent times. The fuzzy entropy scheme has been mostly applied to image thresholding. Fuzzy membership functions are the major source for segmenting the image by fuzzy entropy and thresholding. In the proposed paper, the Teacher-Learner-Based Optimization (TLBO) algorithm is involved in searching for optimal threshold values combination for fruit image segmentation based on fuzzy entropy. The proposed scheme is performed on red apple, green apple, golden apple, guava, and orange fruit images. For comparison of segmentation results of the proposed scheme with existing approaches, PSNR and uniformity parameters are utilized. It has been observed that the performance of the proposed approach is more effective than GA (Genetic Algorithm), BFO (Bacteria Foraging Optimization), and HBMO (Honey Bee Mating Optimization) approaches.