An Improved Quantum-Inspired Genetic Algorithm for Image Multilevel Thresholding Segmentation

A multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm introduces an adaptive adjustment strategy of the rotation angle and a cooperative learning strategy into quantum genetic algorithm (called IQGA). An adaptive adjustment stra...

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Bibliographic Details
Published in:Mathematical problems in engineering 2014-01, Vol.2014 (2014), p.1-12
Main Authors: Zhang, Jian, Li, Huanzhou, Tang, Zhangguo, Lu, Qiuping, Zheng, Xiuqing, Zhou, Jiliu
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Computer science
Cooperative learning
Dimensional stability
Engineering
Entropy
Genetic algorithms
Heuristic
Image processing systems
Image segmentation
Learning
Machine learning
Mathematical analysis
Methods
Multilevel
Rotation
Searching
Segmentation
Solution space
Strategy
Vectors (mathematics)
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Summary:A multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm introduces an adaptive adjustment strategy of the rotation angle and a cooperative learning strategy into quantum genetic algorithm (called IQGA). An adaptive adjustment strategy of the quantum rotation which is introduced in this study helps improving the convergence speed, search ability, and stability. Cooperative learning enhances the search ability in the high-dimensional solution space by splitting a high-dimensional vector into several one-dimensional vectors. The experimental results demonstrate good performance of the IQGA in solving multilevel thresholding segmentation problem by compared with QGA, GA and PSO.
ISSN:1024-123X
1563-5147
DOI:10.1155/2014/295402