An image segmentation method using automatic threshold based on improved genetic selecting algorithm

In this paper, an image segmentation method using automatic threshold based on improved genetic selecting algorithm is presented. Optimal threshold for image segmentation is converted into an optimization problem in this new method. In order to achieve good effects for image segmentation, the optima...

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Bibliographic Details
Published in:Automatic control and computer sciences Vol. 50; no. 6; pp. 432 - 440
Main Authors: Wang, Zhiwen, Wang, Yuhang, Jiang, Lianyuan, Zhang, Canlong, Wang, Pengtao
Format: Journal Article
Language:English
Published: New York Allerton Press 01.11.2016
Springer Nature B.V
Subjects:
Algorithms
Computer Science
Computer vision
Control Structures and Microprogramming
Image segmentation
Normal distribution
Optimization
Parameters
Simulated annealing
Otsu
improved genetic selecting algorithm
automatic threshold
image segmentation
ISSN:0146-4116, 1558-108X
Online Access:Get full text
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Summary:In this paper, an image segmentation method using automatic threshold based on improved genetic selecting algorithm is presented. Optimal threshold for image segmentation is converted into an optimization problem in this new method. In order to achieve good effects for image segmentation, the optimal threshold is solved by using optimizing efficiency of improved genetic selecting algorithm that can achieve a global optimum. The genetic selecting algorithm is optimized by using simulated annealing temperature parameters to achieve appropriate selective pressures. Encoding, crossover, mutation operator and other parameters of genetic selecting algorithm are improved moderately in this method. It can overcome the shortcomings of the existing image segmentation methods, which only consider pixel gray value without considering spatial features and large computational complexity of these algorithms. Experiment results show that the new algorithm greatly reduces the optimization time, enhances the anti-noise performance of image segmentation, and improves the efficiency of image segmentation. Experimental results also show that the new algorithm can get better segmentation effect than that of Otsu’s method when the gray-level distribution of the background follows normal distribution approximately, and the target region is less than the background region. Therefore, the new method can facilitate subsequent processing for computer vision, and can be applied to realtime image segmentation.
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ISSN:0146-4116
1558-108X
DOI:10.3103/S0146411616060092