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- Title
Two-dimensional cross entropy multi-threshold image segmentation based on improved BBO algorithm.
- Authors
LI Wei; HU Xiao-hui; WANG Hong-chuang
- Abstract
In order to improve the global search ability of biogeography-based optimization (BBO) algorithm in multi-threshold image segmentation, a multi-threshold image segmentation based on improved BBO algorithm is proposed. When using BBO algorithm to optimize threshold, firstly, the elitist selection operator is used to retain the optimal set of solutions. Secondly, a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations. Thirdly, to reduce the blindness of traditional mutation operations, a mutation operation through binary computation is created. Then, it is applied to the multi-threshold image segmentation of two-dimensional cross entropy. Finally, this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm. The experimental results show that the method has good convergence stability, it can effectively shorten the time of iteration, and the optimization performance is better than the standard BBO algorithm.
- Subjects
ENTROPY (Information theory); IMAGE segmentation; PARTICLE swarm optimization
- Publication
Journal of Measurement Science & Instrumentation, 2018, Vol 9, Issue 1, p42
- ISSN
1674-8042
- Publication type
Article
- DOI
10.3969/j.issn.1674-8042.2018.01.006