Journal of Jilin University Science Edition

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Image Segmentation Based on Improved Particle Swarm Optimization Algorithm

LIU Yang   

  1. Institute of Cloud Computing and Big Data, Henan University of Economics and Law, Zhengzhou 450046, China
  • Received:2017-06-12 Online:2018-07-26 Published:2018-07-31
  • Contact: LIU Yang E-mail:yanliu.huel@foxmail.com

Abstract: Aimimg at the problem that the active contour model was difficult to achieve high precision segmentation of the image, in order to obtain more ideal image segmentation results, the author proposed a new image segmentation method based on improved particle swarm optimization (PSO) algorithm. Firstly, the traditional active contour model was analyzed, and the limitation of its existence was pointed out. Secondly, the objective function of the energy minimization control point was established, the optimal value of the objective function was searched by the particle swarm optimization algorithm, and the image segmentation was realized according to all the energy minimization control points. Finally, the standard image database and the traditional image segmentation method were uesd for comparative test. The test results show that, compared with the traditional method, the proposed method can segment the images more accurately and quickly. It can effectively suppress the noise interference in the images, and obtain ideal image segmentation effect.

Key words: energy minimization, objective function, active contour model, image segmentation, particle swarm optimization algorithm

CLC Number: 

  • TP391