3DPCNN based on whale optimization algorithm for color image segmentation

Article type: Research Article

Authors: Xing, Zhikai | Jia, Heming^{; *} | Song, Wenlong

Affiliations: Northeast Forestry University, Harbin, China

Correspondence: [*] Corresponding author. Heming Jia, Northeast Forestry University, Harbin, China. E-mail: [email protected].

Abstract: Considering that the 3D pulse-coupled neural network (3D-PCNN) model has the deficiency of high parameter complexity and low accuracy in color image segmentation, swarm intelligence optimization algorithm is adopted to optimize the image segmentation process. In this paper, whale optimization algorithm (WOA) is adopted to optimize the 3D-PCNN model parameters E and β. The improved product cross entropy (IPCE) is chosen as the fitness function of optimization algorithm. WOA algorithm is used to find the minimum fitness function, and the corresponding optimal parameters are obtained. Through the study of image segmentation in the image segmentation library of University of Berkeley and the actual plant canopy image, the maximum entropy value and the Tsallis entropy value are compared and analyzed. Experimental results illustrate that the proposed algorithm can obtain more accurate image segmentation effect and higher segmentation rate.

Keywords: 3D-PCNN, color image segmentation, whale optimization algorithm, improved product cross entropy

DOI: 10.3233/JIFS-182893

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1499-1511, 2019