A Multi-threshold Segmentation Approach Based on Artificial Bee Colony Optimization

TL;DR

This paper introduces a novel multi-threshold image segmentation method using the Artificial Bee Colony algorithm, which efficiently approximates Gaussian mixture models for threshold selection, outperforming traditional methods in convergence speed and robustness.

Contribution

The paper presents a new ABC-based approach for multi-threshold segmentation that offers faster convergence and reduced sensitivity to initial conditions compared to existing algorithms.

Findings

Demonstrates effective automatic multi-threshold selection.

Shows faster convergence than EM algorithm.

Achieves better performance in complex computations.

Abstract

This paper explores the use of the Artificial Bee Colony (ABC) algorithm to compute threshold selection for image segmentation. ABC is a heuristic algorithm motivated by the intelligent behavior of honey-bees which has been successfully employed to solve complex optimization problems. In this approach, an image 1D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. For the approximation scheme, each Gaussian function represents a pixel class and therefore a threshold. Unlike the Expectation Maximization (EM) algorithm, the ABC based method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also improves complex time consuming computations commonly required by gradient-based methods. Experimental results demonstrate the algorithms ability to perform automatic multi threshold selection yet showing interesting advantages by comparison to other well known algorithms.