Abstract
The traditional two-dimensional Otsu algorithm only considers the limitations of the maximum variance of between-cluster variance of the target class and background class; this paper proposes evolutionary game improved algorithm. Algorithm takes full consideration of own pixel cohesion of target and background. It can meet the same of maximum variance of between-cluster variance. To ensure minimum threshold discriminant function within the variance, this kind of evolutionary game algorithm searching space for optimal solution is applied. Experimental results show that the method proposed in this paper makes the detail of segmentation image syllabify and has better antijamming capability; the improved genetic algorithm which used searching optimal solution has faster convergence speed and better global search capability.
Highlights
Image segmentation is the first step in image analysis, understanding, and pattern recognition, which is one of the most important steps
Image segmentation is often used in medical image processing, such as nuclear magnetic resonance image, but it is widely used in geographical space, environmental meteorology, and other fields
Its essence is through the image histogram information to determine the threshold of image segmentation
Summary
Image segmentation is the first step in image analysis, understanding, and pattern recognition, which is one of the most important steps. In the literature [1], the statistical properties based on two-dimensional histogram are proposed to determine the threshold value It includes the gray information of the image pixels. In the traditional two-dimensional Otsu method, the threshold discriminant function (trace of the dispersion matrix) takes into account only the variance of the target class and the background class; that is, the larger the variance between classes, the better the segmentation effect. In this paper, the measure of the dispersion within the class is introduced to the recognition function of threshold This can reflect the efficiency of the overall classification, the following: two classes co and cb existing in two-dimensional histogram and calculated variance of the center of the target class μo. Molecular ωo × ωb × tr(σB) shows the property of between-class variance and denominator ρs performance within the class cohesion; when φs(s, t) obtains the maximum value, effect of the image segment is best.
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