Multi-level image segmentation based on an improved particle swarm optimization with an equilibrium strategy
[1]Xia Xingyu,Gao Hao,Wang Chuangye.Multi-level image segmentation based on an improved particle swarm optimization with an equilibrium strategy[J].Journal of Zhengzhou University (Engineering Science),2018,39(01):59-66.[doi:10.13705/j.issn.1671-6833.2018.01.012]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
39卷
Number of periods:
2018 01
Page number:
59-66
Column:
Public date:
2018-01-20
- Title:
- Multi-level image segmentation based on an improved particle swarm optimization with an equilibrium strategy
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2018.01.012
- Abstract:
- The computation time of some Multi-level threshold segmentation techniques needs were too long to bear, and it grew exponentially with the number of thresholds increased. This paper proposed a particle swarm optimization with an equilibrium strategy for shorting its computation time. First, during iterations, a balance operator for individuals to have more chances to search in the global area was introduced. Furthermore, for enhancing the local search ability of our proposed algorithm, a disturbance operator was also been introduced in this paper which enabled the individual had more opportunities to make a precise search. The improved algorithm enables particles had more chances to jump out of a local area for enhancing their global search ability. Meanwhile, a valuable point to guide the search direction of the particles was introduced. Then it accelerated the convergence rate of the improved algorithm. Kapur method was used in this paper to test the performance of the proposed method. Experimental results showed that our proposed algorithm showed more power and fast search ability when compared with the other population-based algorithms.
- References:
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- Similar References:
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Last Update:
2018-01-23