A comparison of nature inspired algorithms for multi-threshold image segmentation

Abstract

In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class is labeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selection problems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates the histogram, representing a pixel class and therefore a threshold point.Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem.

Multilevel threshold selection for image segmentation using soft computing techniques

“…is more due to exhaust search performed by those algorithms. To reduce the complexity, optimization techniques such as Genetic Algorithm (GA) (Hammouche et al 2008;Indira and Ramesh 2011;Kumar et al 2012;Sridevi et al 2014), particle swarm optimization (Djerou et al 2012;Osuna-Enciso et al 2013), differential evolution algorithm (Pei et al 2009;Ali et al 2014), bee colony optimization (Akay 2013), intelligent techniques (Sathya and Kayalvizhi 2012) have been widely applied. The objective of an optimization problem is to compute a value for the variables such that it should satisfy the given constraints and objective function.…”

Adaptive Computation of Unimodal and Hysteresis Thresholds

“…and ABC algorithms for the image thresholding were explored. 12 These bio-inspired optimal methods always contain iterative steps that imply heavy computation burden so that the calculation complexity is always considered in different areas. [13][14][15][16] Hence, the calculation complexity is considered in this paper.…”

Threshnomics: An Introduction to Threshold Logic in Algorithms and Circuits

“…However, for determining which pixels should be discarded as noise and which should be retained, threshold based criterions are set. Segmentation of images [25][26][27] are also essentially supported by thresholds. While some segmentation methods use fixed threshold values to detect abrupt changes in pixels, some methods use adaptive thresholding techniques.…”