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Rapid developments in swarm intelligence optimizers and computer processingabilities make opportunities to design more accurate, stable, and comprehensivemethods for color image segmentation. This paper presents a new way forunsupervised image segmentation by combining histogram thresholding methods(Kapur's entropy and Otsu's method) and different multi-objective swarmintelligence algorithms (MOPSO, MOGWO, MSSA, and MOALO) to thresholding 3Dhistogram of a color image. More precisely, this method first combines theobjective function of traditional thresholding algorithms to designcomprehensive objective functions then uses multi-objective optimizers to findthe best thresholds during the optimization of designed objective functions.Also, our method uses a vector objective function in 3D space that couldsimultaneously handle the segmentation of entire image color channels with thesame thresholds. To optimize this vector objective function, we employmultiobjective swarm optimizers that can optimize multiple objective functionsat the same time. Therefore, our method considers dependencies between channelsto find the thresholds that satisfy objective functions of color channels(which we name as vector objective function) simultaneously. Segmenting entirecolor channels with the same thresholds also benefits from the fact that ourproposed method needs fewer thresholds to segment the image than otherthresholding algorithms; thus, it requires less memory space to savethresholds. 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 totraditional thresholding methods that separately threshold histograms of acolor image.

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