A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms

Multilevel thresholding for segmentation is an essential task and indispensable process in various applications. Conventional color multilevel thresholding based image segmentations are computationally expensive, and lack accuracy and stability. To address this issue, this paper introduces the comparative performance study of different objective functions using cuckoo search and other optimization algorithms to solve the color image segmentation problem via multilevel thresholding. During the optimization process, solutions are evaluated using Otsu or Kapur's method. Performance of the proposed approach has been assessed using a variety of benchmark images, and compared against three other nature inspired algorithms namely differential evolution (DE), wind driven optimization (WDO) and particle swam optimization (PSO) algorithms. Results have been analyzed both qualitatively and quantitatively, based on the fitness values of obtained best solutions and four popular performance measures namely PSNR, MSE, SSIM and FSIM indices as well. According to statistical analysis of different nature inspired optimization algorithms, Kapur's entropy was found to be more accurate and robust for multilevel colored satellite image segmentation problem. On the other hand, cuckoo search was found to be most promising for colored satellite image segmentation. (C) 2016 Elsevier Ltd. All rights reserved.