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Method for splitting images based on clustering of immunity sparse spectrums

A technology of image segmentation and sparse spectrum, which is applied in the field of image processing, can solve the problems of large amount of calculation and time consumption, slow image segmentation speed, and great difficulty in calculation time complexity, etc., so as to speed up image segmentation speed, segment The results improve and reduce the effect of computational complexity

Inactive Publication Date: 2010-03-17
XIDIAN UNIV
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Problems solved by technology

This is very difficult in practice, both in terms of computation and time complexity.
On the other hand, even if a suitable tolerance is found, the greedy selection method needs to calculate the error of using the selected samples to approximate the current candidate sample one by one. The speed of image segmentation is very slow

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  • Method for splitting images based on clustering of immunity sparse spectrums
  • Method for splitting images based on clustering of immunity sparse spectrums
  • Method for splitting images based on clustering of immunity sparse spectrums

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Embodiment Construction

[0036] refer to figure 1 , the specific implementation process of the present invention is as follows:

[0037] Step 1. Use the gray level co-occurrence matrix to perform feature extraction on the image to be segmented.

[0038] Generate a gray level co-occurrence matrix p for the image to be segmented ij (s, θ), where s is the sample point x i and x j The distance between, the value of θ is 4 discrete directions: 0°, 45°, 90°, 135°, and three statistics are taken in each direction: second-order moment of angle, homogeneous area, contrast, each The statistics are calculated according to the following formula:

[0039] Angular second moment: f 1 = Σ i = 0 N - 1 Σ j = 0 N - ...

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Abstract

The invention discloses a method for splitting images based on clustering of immunity sparse spectrums, mainly solving the problem that a spectrum clustering method has poor stability and high complexity. The method comprises the following steps: (1) extracting features of images to be split; (2) normalizing feature data to eliminate the magnitude effect among data; (3) carrying out real coding onthe normalized feature data; (4) randomly generating initial species groups of the coded data and calculating the affinity; (5) cloning based on the affinity size of an antibody; (6) carrying out Gaussian mutation on the cloned antibody species groups and selecting the antibody of the highest affinity as an input for the next round; (7) iterating the set maximum iterations to obtain a final selected sample subset; (8) carrying out greed spectrum dimensionality reduction on the selected sample subset, clustering the dimensionally reduced data and outputting the final image splitting result. Compared with the prior art, the method has the advantages of no need of priori knowledge, high accuracy and low calculation complexity. The method can be used for object detection and object identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to image segmentation, and can be used for target detection and target recognition on texture images and SAR images. Background technique [0002] Clustering refers to dividing a sample set without category marks into several subsets or categories according to certain criteria, so that similar samples can be classified into one category as much as possible, and dissimilar samples can be divided into different categories as much as possible. Cluster analysis is a kind of multivariate statistical analysis and an important branch of unsupervised pattern recognition. As an unsupervised classification method, cluster analysis has been widely used in many fields such as pattern recognition, data mining, computer vision and fuzzy control. Traditional clustering algorithms, such as k-means algorithm and EM algorithm, are all based on convex spherical sample space, but when the sample sp...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/12G01S13/90G01S7/41
Inventor 缑水平焦李成张佳杨淑媛钟桦吴建设田小林庄雄毛莎莎
Owner XIDIAN UNIV
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