A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation.

Bowen Wu,Jun Cao,Liangkuan Zhu,Jingyu Wang

doi:10.3390/e23121599

Abstract

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.

Highlights

Image segmentation is a vital processing stage in object location and pattern recognition [1]
In the past few years, researchers have proffered a range of methods to achieve image segmentation, which can be summarized as threshold-based method [6], edge-based method [7], region-based method [8], clustering-based method [9], turbopixel/superpixelbased methods [10,11], watershed-based methods [12,13], contour models-based [14,15], and artificial neural network-based methods [16]
The hybrid preaching optimization algorithm (HPOA)-based segmentation algorithm is employed in color images

Summary

Introduction

Image segmentation is a vital processing stage in object location and pattern recognition [1]. It can be deemed as a technique that partitions the components of an image into several disjoint categories concerning color, feature, texture, etc. This work can be divided into color image segmentation and gray image segmentation. The threshold technique has become the most in vogue method compared with other methods for its simple implementation and high accuracy [17]. It consists of bi-level and multilevel segmentation depending on the number of thresholds.

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Discussion

Conclusion