Research Area:  Metaheuristic Computing

Abstract:

Thresholding is an important and well-known technique that plays a major role in distinguishing the image objects from its background. In the other hand, separating the images into several different regions by determining multiple threshold values is called multilevel image thresholding. The Kapur entropy thresholding and maximum between-class variance (Otsu) have been widely used in image thresholding. However, these methods are computationally expensive and with increase in level numbers computational complexity increase exponentially. To overcome this problem, this paper presents animal migration optimization algorithm for multilevel thresholding. For evaluating the efficiency of proposed method, various benchmark images are used for carrying out the experiments, and obtained results via animal migration optimization algorithm compared with most popular optimization technique such as Particle Swarm Optimization, Genetic and bacterial foraging algorithm. Experimental results figure out that the proposed method provides better result than the other tested algorithms.

Keywords:  
entropy
thresholding
computational complexity
migration optimization algorithm
benchmark images
Particle Swarm Optimization
Genetic
bacterial foraging

Author(s) Name:  Taymaz Rahkar Farshi

Publisher name:  Springer

DOI:  10.1007/s42044-018-0022-5

Volume Information:  2, pages 9–22

Paper Link:   https://link.springer.com/article/10.1007/s42044-018-0022-5