Color Image Segmentation Using Multi-Objective Swarm Optimizer and Multi-level Histogram Thresholding

TL;DR

This paper introduces a novel unsupervised color image segmentation method combining multi-objective swarm optimization with multi-level histogram thresholding, effectively handling all color channels simultaneously and requiring fewer thresholds for improved accuracy and efficiency.

Contribution

It proposes a new multi-objective swarm optimization approach that optimizes a vector objective function for color image segmentation, considering channel dependencies and reducing threshold count.

Findings

Outperforms traditional thresholding methods in accuracy.

Requires fewer thresholds, saving memory.

Demonstrates superiority through subjective and objective evaluations.

Abstract

Rapid developments in swarm intelligence optimizers and computer processing abilities make opportunities to design more accurate, stable, and comprehensive methods for color image segmentation. This paper presents a new way for unsupervised image segmentation by combining histogram thresholding methods (Kapur's entropy and Otsu's method) and different multi-objective swarm intelligence algorithms (MOPSO, MOGWO, MSSA, and MOALO) to thresholding 3D histogram of a color image. More precisely, this method first combines the objective function of traditional thresholding algorithms to design comprehensive objective functions then uses multi-objective optimizers to find the best thresholds during the optimization of designed objective functions. Also, our method uses a vector objective function in 3D space that could simultaneously handle the segmentation of entire image color channels with the same thresholds. To optimize this vector objective function, we employ multiobjective swarm optimizers that can optimize multiple objective functions at the same time. Therefore, our method considers dependencies between channels to find the thresholds that satisfy objective functions of color channels (which we name as vector objective function) simultaneously. Segmenting entire color channels with the same thresholds also benefits from the fact that our proposed method needs fewer thresholds to segment the image than other thresholding algorithms; thus, it requires less memory space to save thresholds. It helps a lot when we want to segment many images to many regions. The subjective and objective results show the superiority of this method to traditional thresholding methods that separately threshold histograms of a color image.