Minimum cross Entropy Thresholding based apple image segmentation using Teacher Learner Based Optimization Algorithm

摘要

Image segmentation play a vital role in classification. In this paper, to minimize cross entropy, Teacher-learner optimization approach is engaged to explore an optimal sequence of threshold values at distinct levels. One of the most prominent ideas in image segmentation is image thresholding. The suggested scheme is influenced by the shift of advice in the classroom framework, where a leaner hears from the scholar and later corresponds with each alternative.This idea is practiced to search optimal threshold values from fruit images at various levels.The proposed approach exploited the information theory approach called minimum cross entropy. For experiment work, fruit images (red, green, and golden apple) are adopted. PSNR and uniformity performance procedures are exploited to match the evaluation of TLBO - MCET with GA - MCET, and HBMO - MCET.