DocumentCode
686334
Title
Image segmentation using Shadowed C-Means and Kernel method
Author
Long Chen ; Jing Zou ; Chen, C.L.P.
Author_Institution
Fac. of Sci. & Technol., Univ. of Macau, Macau, China
fYear
2013
fDate
6-8 Dec. 2013
Firstpage
374
Lastpage
379
Abstract
A new shadowed c-means clustering based image segmentation method is proposed in this paper. By including the local spatial information in shadowed c-means algorithm and mapping the original data into a high dimensional space via kernel method, we propose the Kernel Spatial Shadowed C-Means (KSSCM) clustering algorithm for image segmentation problems. The KSSCM based approach shows better performance than traditional clustering based approaches on segmenting noised synthetic and real images.
Keywords
image segmentation; pattern clustering; KSSCM; high dimensional space; image segmentation method; kernel method; kernel spatial shadowed c-means clustering algorithm; local spatial information; noised synthetic image segmentation; real images; Accuracy; Clustering algorithms; Educational institutions; Image segmentation; Kernel; Noise; Rician channels; Fuzzy clustering; Image segmentation; Kernel method; Shadowed c-means; Spatial information;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Theory and Its Applications (iFUZZY), 2013 International Conference on
Conference_Location
Taipei
Type
conf
DOI
10.1109/iFuzzy.2013.6825468
Filename
6825468
Link To Document