TY - GEN
T1 - Kernel fuzzy similarity measure-based spectral clustering for image segmentation
AU - Yang, Yifang
AU - Wang, Yuping
AU - CHEUNG, Yiu Ming
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - Spectral clustering has been successfully used in the field of pattern recognition and image processing. The efficiency of spectral clustering, however, depends heavily on the similarity measure adopted. A widely used similarity measure is the Gaussian kernel function where Euclidean distance is used. Unfortunately, the Gaussian kernel function is parameter sensitive and the Euclidean distance is usually not suitable to the complex distribution data. In this paper, a novel similarity measure called kernel fuzzy similarity measure is proposed first, Then this novel measure is integrated into spectral clustering to get a new clustering method: kernel fuzzy similarity based spectral clustering (KFSC). To alleviate the computational complexity of KFSC on image segmentation, Nystr m method is used in KFSC. At last, the experiments on three synthetic texture images are made, and the results demonstrate the effectiveness of the proposed algorithm.
AB - Spectral clustering has been successfully used in the field of pattern recognition and image processing. The efficiency of spectral clustering, however, depends heavily on the similarity measure adopted. A widely used similarity measure is the Gaussian kernel function where Euclidean distance is used. Unfortunately, the Gaussian kernel function is parameter sensitive and the Euclidean distance is usually not suitable to the complex distribution data. In this paper, a novel similarity measure called kernel fuzzy similarity measure is proposed first, Then this novel measure is integrated into spectral clustering to get a new clustering method: kernel fuzzy similarity based spectral clustering (KFSC). To alleviate the computational complexity of KFSC on image segmentation, Nystr m method is used in KFSC. At last, the experiments on three synthetic texture images are made, and the results demonstrate the effectiveness of the proposed algorithm.
KW - image segmentation
KW - kernel fuzzy-clustering
KW - Nyström method
KW - spectral clustering
UR - http://www.scopus.com/inward/record.url?scp=84880689129&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39342-6_27
DO - 10.1007/978-3-642-39342-6_27
M3 - Conference proceeding
AN - SCOPUS:84880689129
SN - 9783642393419
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 246
EP - 253
BT - Human-Computer Interaction
T2 - 15th International Conference on Human-Computer Interaction, HCI International 2013
Y2 - 21 July 2013 through 26 July 2013
ER -