Optimal Multilevel image thresholding using the teaching-learning-based optimization

Document Type : Research Paper

Authors

1 1Department of Computer Engineering, Faculty of Computer and Electrical Engineering, University of Kashan

2 2Department of Computer Engineering, Faculty of Computer and Electrical Engineering, University of Kashan

Abstract

Image thresholding is a popular method for image segmentation. Histogram is used for image segmentation in image thresholding. In this paper, a multilevel image thresholding is proposed based on teaching-learning-based optimization (TLBO). TLBO is a new population-based metaheuristic inspired by learners and teacher in a classroom. The optimal thresholds are found by maximizing Kapur’s (entropy criterion) thresholding function. The performance of TLBO is explained by considering five images. In addition, the performance is compared with three well known population-based metaheuristics: particle swarm optimization(PSO), genetic algorithm (GA), and differential evolution (DE). Results show that TLBO presents the better performance in terms of fitness value, peak signal to noise ratio (PSNR), Structural-Similarity index (SSIM), and stability.

Keywords

Journal of Machine Vision and Image Processing
Volume 2, Issue 2 - Serial Number 2
January 2016
Pages 51-62

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