Image segmentation method based on genetic rough set C-mean clustering

A mean clustering and image segmentation technology, applied in image enhancement, image data processing, instruments, etc., can solve the problems of losing local information, weakening the recognition of insignificant objects in images, etc., to improve robustness and reliability, accurate images, etc. Segmentation results, the effect of reducing misclassified areas

Active Publication Date: 2013-08-14
探知图灵科技(西安)有限公司
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Problems solved by technology

This method has strong anti-noise ability, fast convergence speed, and can improve the stability of the image segmentation effect. However, the disadvantage of this method is that it only uses the neighborhood information of the image, and it loses too much due to over-smoothing in the case of complex images. Local information, which weakens the ability to identify inconspicuous objects in the image

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  • Image segmentation method based on genetic rough set C-mean clustering
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  • Image segmentation method based on genetic rough set C-mean clustering

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[0050] Attached below figure 1 The steps of the present invention are further described.

[0051] Step 1, input an image to be segmented

[0052] Step 2, extract image texture features

[0053] First, the wavelet decomposition method is used to extract the first 10-dimensional features of all pixels of the image to be segmented;

[0054] The wavelet decomposition method uses a three-layer wavelet transform with a window size of 32×32 on the image to obtain the wavelet features composed of subband coefficients, which are used as the first 10-dimensional wavelet feature vector of each pixel.

[0055] Then, use the gray level co-occurrence matrix method to extract the last 12-dimensional features of all pixels of the image to be segmented;

[0056] The steps of the gray level co-occurrence matrix method are as follows:

[0057] Vectorize the image into L=16 gray levels, and then sequentially set the angles between the two pixel points and the horizontal axis to be 0°, 45°, 90...

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Abstract

The invention discloses an image segmentation method based on genetic rough set C-mean clustering, which mainly solves the problem that the conventional method has poor robustness, easily falls into local optimum and loses too much local information. The method comprises the implementation steps of: (1) inputting a to-be-segmented image; (2) extracting image texture features; (3) generating clustering object data; (4) initializing population; (5) updating membership; (6) dividing the clustering object data; (7) updating the population; (8) calculating an individual fitness value; (9) evolving the population; (10) judging whether a termination condition is satisfied; (11) generating an optimal individual; (12) marking; (13) generating segmented images. In the method, the texture features of each pixel of the image are extracted, and the texture features are marked through the C-mean clustering method based on the genetic algorithm and the thought of rough set so as to divide the pixels, thus, stability of image segmentation is improved, and more accurate image segmentation result is obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an improved generalized fuzzy c-means clustering algorithm based on GA and rough set in the technical field of image segmentation. The invention can be used to segment synthetic aperture radar SAR images and natural images to achieve the purpose of target recognition. Background technique [0002] Applying intelligent computing technology to image segmentation is a popular research direction in the field of image segmentation in recent years, mainly including neural network, genetic algorithm, swarm intelligence algorithm and artificial immune system framework. From the perspective of segmentation results, the process of image segmentation is to assign a label to each pixel, which reflects the category of the pixel in the segmentation result. As long as the labels of these features are found, the classification of pixels can be realized, and the result of image se...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
Inventor 马文萍焦李成葛小华公茂果马晶晶
Owner 探知图灵科技(西安)有限公司
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