Abstract
Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.
Highlights
Image segmentation is process of subdivision of an image into homogeneous and disjoint sets sharing similar properties such as intensity, color, and contours
The multilevel image thresholding problem deals with finding optimal thresholds within the range [0, L − 1] that maximize the functions defined by (3) and (6)
To compare the proposed improved hybridized bat algorithm (IBA) algorithm with particle swarm optimization (PSO), differential evolution (DE), cuckoo search (CS), firefly algorithm (FA) [49], and bat algorithm (BA) algorithms, the objective function evaluation was computed N × G times, where N is the population size and G is the maximum number of generations
Summary
Image segmentation is process of subdivision of an image into homogeneous and disjoint sets sharing similar properties such as intensity, color, and contours. There are many different methods for computing the thresholds for an image such as maximizing the gray level variance [7], entropy [8], similarity [9], and measure of fuzziness [10]. Using the entropy of the histogram, Pun was the first to introduce a new method for gray level image thresholding [8]. To perform the separation of classes, Otsu has developed a thresholding method for computing the optimal thresholds by maximizing the betweenclass variance using an exhaustive search [7]. Several metaheuristic algorithms derived from the behavior of biological and physical systems in the nature have been proposed as powerful methods for searching the multilevel image thresholds.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
