Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

• Main Authors: L. DJEROU, N. KHELIL, N. H. DEHIMI, M. BATOUCHE
• Format: Article
• Language: English
• Published: Stefan cel Mare University of Suceava 2012-01-01
• Series: Journal of Applied Computer Science & Mathematics
• Subjects: Binary Particle Swarm Optimization; Image Segmentation; Image Thresholding; Multi-objective Optimization; Non-pare To Approach
• Online Access: http://jacs.usv.ro/getpdf.php?paperid=13_4
• ISSN: 2066-4273; 2066-3129

In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.