Paper Info
Title
Patch-based fuzzy clustering for image segmentation
Abstract
Fuzzy C-means has been adopted for image segmentation, but it is sensitive to noise and other image artifacts due to not considering neighbor information. In order to solve this problem, many improved algorithms have been proposed, such as fuzzy local information C-means clustering algorithm (FLICM). However, the segmentation results of FLICM are unsatisfactory when performed on complex images. To overcome this, a novel fuzzy clustering algorithm is proposed in this paper, and more information is utilized to guide the procedure of image segmentation. In the proposed algorithm, pixel relevance based on patch similarity will be investigated firstly, by which all information over the whole image can be considered, not limited to local context. Compared with Zhang et al. (Multimed Tools Appl 76(6):7869–7895, 2017a.  https://doi.org/10.1007/s11042-016-3399-x) pixel relevance is unnecessary to be normalized, and much more information can play positive role in image segmentation. Experimental results show that the proposed algorithm outperforms current fuzzy algorithms, especially in enhancing the robustness of corresponding fuzzy clustering algorithms.
Year
DOI
Venue
2019
10.1007/s00500-017-2955-2
soft computing
Keywords
Field
DocType
Image segmentation, Fuzzy clustering, FLICM, Pixel relevance, Patch similarity
Fuzzy clustering,Normalization (statistics),Pattern recognition,Segmentation,Computer science,Fuzzy logic,Robustness (computer science),Image segmentation,Artificial intelligence,Pixel,Cluster analysis,Machine learning
Journal
Volume
Issue
ISSN
23
SP9
1433-7479
Citations 
PageRank 
References 
1
0.35
25
Authors
9
Name
Order
Citations
PageRank
Xiaofeng Zhang1788.90
Xiaofeng Zhang2788.90
Qiang Guo310.35
Yujuan Sun4262.37
Hui Liu53910.58
Gang Wang634497.03
Qingtang Su717616.90
Caiming Zhang851.75
Caiming Zhang944688.19