TY - JOUR
T1 - Color image segmentation using fuzzy C-means and eigenspace projections
AU - Yang, Jar Ferr
AU - Hao, Shu Sheng
AU - Chung, Pau Choo
N1 - Funding Information:
This research was partially supported by the National Science Council under Contract #NSC-88-2213-E-006-104 and by the Image/Graphics Technology Research and Application Development Project of Institute for Information Industry sponsored by MOEA, Taiwan, Republic of China.
PY - 2002/3
Y1 - 2002/3
N2 - In this paper, we propose two eigen-based fuzzy C-means (FCM) clustering algorithms to accurately segment the desired images, which have the same color as the pre-selected pixels. From the selected color pixels, we can first divide the color space into principal and residual eigenspaces. Combined eigenspace transform and the FCM method, we can effectively achieve color image segmentation. The separate eigenspace FCM (SEFCM) algorithm independently applies the FCM method to principal and residual projections to obtain two intermediate segmented images and combines them by logically selecting their common pixels. Jointly considering principal and residual eigenspace projections, we then suggest the coupled eigen-based FCM (CEFCM) algorithm by using an eigen-based membership function in clustering procedure. Simulations show that the proposed SEFCM and CEFCM algorithms can successfully segment the desired color image with substantial accuracy.
AB - In this paper, we propose two eigen-based fuzzy C-means (FCM) clustering algorithms to accurately segment the desired images, which have the same color as the pre-selected pixels. From the selected color pixels, we can first divide the color space into principal and residual eigenspaces. Combined eigenspace transform and the FCM method, we can effectively achieve color image segmentation. The separate eigenspace FCM (SEFCM) algorithm independently applies the FCM method to principal and residual projections to obtain two intermediate segmented images and combines them by logically selecting their common pixels. Jointly considering principal and residual eigenspace projections, we then suggest the coupled eigen-based FCM (CEFCM) algorithm by using an eigen-based membership function in clustering procedure. Simulations show that the proposed SEFCM and CEFCM algorithms can successfully segment the desired color image with substantial accuracy.
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U2 - 10.1016/S0165-1684(01)00196-7
DO - 10.1016/S0165-1684(01)00196-7
M3 - Article
AN - SCOPUS:0036501653
VL - 82
SP - 461
EP - 472
JO - Signal Processing
JF - Signal Processing
SN - 0165-1684
IS - 3
ER -