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Krill Herd Algorithm for Color Image Segmentation with Kapur, Otsu and Minimum Cross Entropy Fitness Functions

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Color image segmentation is essential to analyze information from the desired image with RGB color space. Generally, visual information can be easily retrieved through the simple, effective technique called thresholding. Segmentation of complex images is accurately achieved, through MultiLevel Thresholding (MLT) with most optimistic objective functions such as Kapur, Otsu and Minimum Cross Entropy (MCE), than Bilevel Thresholding (BLT). However, the complexity in exploring the optimal threshold increases with the increase in levels of threshold. The key to breach this barrier is by the most computationally effective, flexible Krill Herd Algorithm (KHA). The behavior of the krill movement, the foraging activity and the diffusion methods are utilized for global and local searches. KHA incorporates the swarm intelligence and with crossover, mutation operators improve the convergence rate. The performance of the KHA is compared with Teaching-Learning Based Optimization (TLBO) and cuckoo search algorithm (CSA). Experimental results disclose that the Otsu based MLT outperforms the Kapur and the MCE fitness functions. Quantitative and qualitative validations by metrics such as computational time, Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) confirm that Kapur, Otsu and MCE based KHA outperform the existing techniques for real life applications.
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Kapur; Krill Herd; Minimum Cross Entropy; Otsu; Thresholding; Teaching-Learning

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