Method of image segmentation based on immune spectrum clustering

An image segmentation and spectral clustering technology, applied in the field of image processing, can solve problems such as difficult implementation, poor results, initialization sensitive local optimum, etc.

Inactive Publication Date: 2008-11-05
XIDIAN UNIV
View PDF0 Cites 33 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this classic spectral clustering algorithm uses k-means clustering in the mapping domain. The k-means itself has the disadvantages of being sensitive to initialization, easy to fall into local optimum, and difficult to implement. Therefore, it is used for image segmentation. poor result

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method of image segmentation based on immune spectrum clustering
  • Method of image segmentation based on immune spectrum clustering
  • Method of image segmentation based on immune spectrum clustering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] refer to figure 2 , the implementation process of the present invention is as follows:

[0038] Step 1, extract the texture features of the input image, and represent each pixel in the image with a feature vector to obtain the image feature set S.

[0039] Input an image, first calculate its gray level co-occurrence matrix, and then calculate the correlation coefficient, entropy and energy characteristics of each pixel from the gray level co-occurrence matrix as the texture feature of the image; at the same time, perform three-level non-subsampling on this image Wavelet transform calculates the energy feature of each pixel, and these two features together constitute the texture feature set S of the image.

[0040] Step 2, use spectral clustering to map the feature set S to a linear measure space, and obtain its mapping set Y.

[0041]1. Use for images that extract texture features The approximation method calculates the similarity matrix W of the feature set S:

...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an image segmentation method based on immunity spectrum clustering, which includes: 1. extracting texture characteristic of the input image, representing each pixel point in the image with an eigenvector to obtain a characteristic set; 2. mappings the characteristic set to a linear measure space by spectrum clustering to a mapping set; 3. dividing the category number according to the given image, accidentally selecting the corresponding number of data from the mapping set as the initial clustering center, executing cloning, variation, selection and judgement in sequence, to find out a optimum clustering center with the same category number with the initial clustering center; 4. dividing all pixel points of the characteristic set to an optimum clustering center nearest to the pixel points, and giving each pixel point a category mark according to the category of optimum clustering center where the pixel point locates to complete the image segmentation. Compared with the prior technology, the invention has advantages of insensitivity to initialization, quick convergence to global optimum and high specification accuracy, which can be used in the image segmentation of SAR image processing and computer visual sense field.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image segmentation method, which can be used for image segmentation in the fields of SAR image processing and computer vision. Background technique [0002] Image segmentation is one of the basic and key technologies in image processing and computer vision. It refers to dividing an image into regions with different characteristics. A region in an image refers to an interconnected pixel with consistent "meaningful" attributes. gather. In many machine vision and image processing algorithms, a simple assumption is made on image features: the intensity of local areas of the image is uniform, and different areas have different intensities. However, there are also many images of actual objects that do not satisfy this assumption, such as wood surfaces, hair, weaves, grass, and sandy beaches. "texture", where distinct regions in an image are identified based on texture rathe...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46G06T7/00G06T5/00
Inventor 张向荣焦李成骞晓雪公茂果李阳阳侯彪马文萍刘若辰
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap