[1]JIA Heming,PENG Xiaoxu,XING Zhikai,et al.Renyi entropy based on improved firefly optimization algorithm for image segmentation of waste oil[J].CAAI Transactions on Intelligent Systems,2020,15(2):367-373.[doi:10.11992/tis.201809002]
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Renyi entropy based on improved firefly optimization algorithm for image segmentation of waste oil

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 2 Page number: 367-373 Column: 学术论文—人工智能基础 Public date: 2020-03-05
Title:
Renyi entropy based on improved firefly optimization algorithm for image segmentation of waste oil
Author(s):
JIA Heming1;  PENG Xiaoxu1;  XING Zhikai1; 2;  LI Jinduo1;  KANG Lifei1
1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China;
2. Daqing Oil Field Co. Oil Production Plant Two, Daqing 163000, China
Keywords:
image processing of waste oil; threshold segmentation; firefly algorithm; two-dimensional Renyi entropy; chaos optimization; multi-objective optimization; fitness learning; global optimization
CLC:
TP391.41
DOI:
10.11992/tis.201809002
Abstract:
Aiming at the problem that the traditional Renyi entropy method has large image gaps and cannot be optimized according to different images when dividing dirty oil images, an improved firefly algorithm is proposed to solve the above problem by optimizing the alpha value of two-dimensional Renyi entropy segmentation algorithm. First, we analyze the characteristics of an acquired oil image and the necessity of segmenting a dirty oil picture; second, aiming at the problems of low optimization precision and slow convergence speed in the later stage, the firefly algorithm is improved to make the initial position of the firefly chaos optimization processing results reach the global optimum, and then Renyi entropy image segmentation algorithm based on the improvement of the firefly algorithm is applied to the experiments of threshold value segmentation of the waste oil image. Finally, the algorithm proposed in this paper is used to collect oil image segmentation in experiments, and the results are compared with the 2D Renyi entropy segmentation and the particle swarm optimization (PSO) Renyi entropy segmentation method. The experimental results illustrate that the proposed algorithm can effectively segment the waste oil area and quickly achieve accurate processing of complex images.
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