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Collective Neurodynamic Optimization for Image Segmentation by Binary Model with Constraints

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Abstract

Threshold method is an important image segmentation method, which has been widely used in image segmentation. For this method, it is very important to choose a good threshold. The traditional threshold segmentation algorithm is implemented by exhaustive method, which makes the solution efficiency very low. This paper presents a collective neurodynamic optimization algorithm to solve the problem of binary optimization in the image segmentation. The problem of image segmentation based on threshold is transformed into binary optimization with constraints. Then, a collective neurodynamic optimization algorithm is introduced which combined with feedback neural network and particle swarm optimization (PSO) algorithm. And the linear programming relaxation constraint method is used to relax binary constraints. It is proved by numerical simulation that the feedback neural network algorithm can converge to the exact local optimal solution of the model and the PSO algorithm can get a better local optimal solution. Finally, several sets of comparative experiments are presented. The feasibility of our proposed method is verified; the experimental results demonstrate the effectiveness of our approach in image segmentation. In this study, a collective neurodynamic optimization was proposed for the image segmentation problem. In the future, we expect that multiple centralized neurodynamic models and intelligent algorithms can be used to solve the problem and improve the convergence speed of the solved model.

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Funding

This work was supported by the Fundamental Research Funds for the Central Universities (Project No. XDJK2020TY003), the Natural Science Foundation of China (Grant No: 61773320), and the Natural Science Foundation Project of Chongqing CSTC (Chongqing Science and Technology Commission) (Grants No. cstc2018jcyjAX0583 and No. cstc2018jcyjAX0810).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Shengzhan He, Junjian Huang & Xing He

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  1. Shengzhan He
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  2. Junjian Huang
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  3. Xing He
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Correspondence to Xing He.

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He, S., Huang, J. & He, X. Collective Neurodynamic Optimization for Image Segmentation by Binary Model with Constraints. Cogn Comput 12, 1265–1275 (2020). https://doi.org/10.1007/s12559-020-09762-0

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