A Benchmark of Population-Based Metaheuristic Algorithms for High-Dimensional Multi-Level Image Thresholding

Abstract

Multi-level image thresholding is a popular approach for image segmentation where the image is divided into several non-overlapping regions based on the image histogram. Conventional algorithms for multi-level image thresholding are time-consuming. This is in particular so when the number of thresholds increases due to the curse of dimensionality where the search space expands exponentially as the number of parameters (thresholds) increases. One approach to address this problem is to employ population-based metaheuristic algorithms. Since various such optimisation algorithms have been presented in the literature, in this paper, we benchmark the performance of 13 population-based algorithms in the high-dimensional search spaces of the multi-level image thresholding problem. The algorithms we assess include the whale optimisation algorithm (WOA), grey wolf optimiser (GWO), cuckoo optimisation algorithm (COA), biogeography-based optimisation (BBO), teaching-learning-based optimisation (TLBO), gravitational search algorithm (GSA), imperialist competitive algorithm (ICA), cuckoo search (CS), firefly algorithm (FA), bat algorithm (BA), differential evolution (DE), particle swarm optimisation (PSO), and genetic algorithm (GA). We evaluate these on different images with regards to objective function value as well as peak signal-to-noise ratio (PSNR) and also employ a non-parametric statistical test, the Wilcoxon signed rank test, to compare the algorithms and to draw conclusions about their performance for multi-level image thresholding.