Image segmentation method adopting semi-supervised RFLICM (Robust Fuzzy Local Information C-Means) clustering on basis of seed set

A technology of image segmentation and clustering method, applied in the field of image processing, can solve the problem that the algorithm is easy to fall into local optimum

Active Publication Date: 2014-04-02
陕西国博政通信息科技有限公司
View PDF1 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, since its initial clustering center is also randomly selected, the algorithm is also prone to fall into local optimum.

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
  • Image segmentation method adopting semi-supervised RFLICM (Robust Fuzzy Local Information C-Means) clustering on basis of seed set
  • Image segmentation method adopting semi-supervised RFLICM (Robust Fuzzy Local Information C-Means) clustering on basis of seed set
  • Image segmentation method adopting semi-supervised RFLICM (Robust Fuzzy Local Information C-Means) clustering on basis of seed set

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] refer to figure 1 , the specific implementation process of the present invention is as follows:

[0035] Step 101: start the image segmentation method based on the semi-supervised RFLICM clustering of the seed set.

[0036] Step 102: Import the image to be segmented, marked as A;

[0037] Step 103: Adding noise to image A;

[0038] Step 104: clustering the noise-added image using the semi-supervised RFLICM clustering method based on the seed set, and the clustering is the final segmentation result of the image.

[0039] Step 105: End the image segmentation method based on the semi-supervised RFLICM clustering of the seed set.

[0040] refer to figure 2 shown.

[0041] Described step 104 comprises the following steps:

[0042] Step 201: Start to cluster the noised image using the semi-supervised RFLICM clustering method based on the seed set.

[0043] Step 202: Initialization: Given a dataset X=[X with partially labeled data b x u ], initialize c, m, iteration...

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 relates to an image segmentation method adopting semi-supervised RFLICM (Robust Fuzzy Local Information C-Means) clustering on the basis of a seed set, which is characterized by at least comprising the following steps of: S101, starting the image segmentation method on the basis of semi-supervised RFLICM clustering of the seed set; S102, leading in an image to be segmented and marking the image as A; S103, carrying out noise adding processing; S104, carrying out clustering on the image added with noise by using a semi-supervised RFLICM clustering method on the basis of the seed set, wherein a clustering result is a final segmentation result of the image; and S105, ending the image segmentation method on the basis of semi-supervised RFLICM clustering of the seed set. The method not only shows the advantages of semi-supervised clustering in the clustering process, but also utilizes an RFLICM algorithm to add local space information and gray information, and thus, the algorithm can utilize more local texture information. Therefore, the image segmentation method has good robustness for noise and a profile and can well kep details of the image, so that accuracy is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to image segmentation, in particular to an image segmentation method based on seed set semi-supervised RFLICM clustering, which can be used for target recognition and target detection on noisy artificial images, natural images and SAR images. Background technique [0002] Cluster analysis is a multivariate analysis method in mathematical statistics. It uses mathematical methods to quantitatively determine the relationship between samples, so as to objectively classify their types. Fuzzy clustering is a clustering algorithm based on fuzzy theory, which classifies the data in the data set according to its degree of membership relative to the cluster center. Fuzzy C-Means (FCM) is a commonly used unsupervised clustering algorithm, which has been widely used in pattern classification, medical image segmentation and other fields. However, since the standard FCM clustering algorith...

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
IPC IPC(8): G06T7/00G06K9/62
Inventor 尚荣华焦李成李巧凤公茂果吴建设李巧兰李阳阳马文萍马晶晶
Owner 陕西国博政通信息科技有限公司
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