A hybrid differential evolution for optimal multilevel image thresholding. (15th December 2016)

• Record Type: Journal Article
• Authors: Mlakar, Uroš; Potočnik, Božidar; Brest, Janez
• Is Part Of: Expert systems with applications. Volume 65(2016)
• Page Start: 221
• Page End: 232
• Publication Date: 2016-12-15
• Subjects: Image segmentation -- Differential evolution -- Multilevel thresholding -- Otsu criterion -- Hybridization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.08.046
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Real test images were used to perform thresholding using Otsu's method with a high level of thresholds. The proposed hybrid is compared with DE, jDE, PSO, ABC, and CS. Algorithms are compared based on PSNR and SSIM metrics. Friedman and Wilcoxon statistical tests are used to show the performances. Proposed hjDE shows superior performance in the quality of the results. Abstract: Image thresholding is a process for separating interesting objects within an image from their background. An optimal threshold's selection can be regarded as a single objective optimization problem, where obtaining a solution can be computationally expensive and time-consuming, especially when the number of thresholds increases greatly. This paper proposes a novel hybrid differential evolution algorithm for selecting the optimal threshold values for a given gray-level input image, using the criterion defined by Otsu. The hybridization is done by adding a reset strategy, adopted from the Cuckoo Search, within the evolutionary loop of differential evolution. Additionally a study of different evolutionary or swarm-based intelligence algorithms for the purpose of thresholding, with a higher number of thresholds was performed, since many real-world applications require more than just a few thresholds for further processing. Experiments were performed on eleven real world images. The efficiency of the hybrid was compared to the cuckoo search and self-adaptive differential evolution, the original differential evolution, particle swarm optimization, and artificial bee colony where the results showed the superiority of the hybrid in terms of better segmentation results with the increased number of thresholds. Since the proposed method needs only two parameters adjusted, it is by far a better choice for real-life applications.