Fuzzy C-means clustering with weighted energy function in MRF for image segmentation

Author

Chi Wang ; Jia Liu ; Maoguo Gong ; Licheng Jiao ; Jing Liu

Author_Institution

Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xian, China

fYear

2014

fDate

6-11 July 2014

Firstpage

210

Lastpage

215

Abstract

In this paper, we present a new Markov Random Field based FCM image segmentation algorithm. A new energy function is proposed to utilize the spatial and contextual information simultaneously. In the proposed energy function, we use a weighted distance to reflect the different effects of neighborhood pixels. By using the new energy function, the new algorithm has a better performance in noise-corrupted images. Experimental results on real and synthetic images show our method is effective.

Keywords

Markov processes; fuzzy set theory; image segmentation; pattern clustering; random processes; FCM image segmentation algorithm; MRF; Markov random field; contextual information; fuzzy C-means clustering; neighborhood pixel effects; noise-corrupted images; real images; spatial information; synthetic images; weighted distance; weighted energy function; Algorithm design and analysis; Clustering algorithms; Image segmentation; Linear programming; Noise; Noise measurement; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-2073-0

Type

conf

DOI

10.1109/FUZZ-IEEE.2014.6891657

Filename

6891657