Home   >   CSC-OpenAccess Library   >    Manuscript Information
Segmentation by Fusion of Self-Adaptive SFCM Cluster in Multi-Color Space Components
kun chen, Yan Ma, Jun Liu
Pages - 157 - 166     |    Revised - 15-03-2012     |    Published - 16-04-2012
Volume - 6   Issue - 2    |    Publication Date - April 2012  Table of Contents
Color Image Segmentation, Histogram, SFCM, Fusion
This paper proposes a new, simple, and efficient segmentation approach that could find diverse applications in pattern recognition as well as in computer vision, particularly in color image segmentation. First, we choose the best segmentation components among six different color spaces. Then, Histogram and SFCM techniques are applied for initialization of segmentation. Finally, we fuse the segmentation results and merge similar regions. Extensive experiments have been taken on Berkeley image database by using the proposed algorithm. The results show that, compared with some classical segmentation algorithms, such as Mean-Shift, FCR and CTM, etc, our method could yield reasonably good or better image partitioning, which illustrates practical value of the method.
CITED BY (4)  
1 Alkama, S., Chahir, Y., & Berkani, D. (2015). Label maps fusion for the marginal segmentation of multi-component images.neural network world, 25(4), 405.
2 Tan, K. S., Isa, N. A. M., & Lim, W. H. (2013). Color image segmentation using adaptive unsupervised clustering approach. Applied Soft Computing, 13(4), 2017-2036.
3 Tan, K. S., Lim, W. H., & Isa, N. A. M. (2013). Novel initialization scheme for Fuzzy C-Means algorithm on color image segmentation. Applied Soft Computing, 13(4), 1832-1852.
4 Xess, M., & Agnes, S. A. (2012). Survey on Clustering based Color Image Segmentation and novel approaches to FCM Algorithm. International Journal of Research in Engineering and Technology, 1(1), 346-349.
1 Google Scholar 
2 CiteSeerX 
3 Scribd 
4 SlideShare 
5 PdfSR 
A. Y. Yang, J. Wright, S. Sastry, and Y. Ma. “Unsupervised Segmentation Of Natural Images Via Lossy Data Compression”. Comput. Vis. Image Understand, vol. 110, pp. 212-225, 2008.
D. Comanicu, P. Meer. “Mean shift: A Robust Approach Toward Feature Space Analysis”. IEEE
D. Martin, C. Fowlkes, D. Tal, et al. “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proc. of the 8th IEEE International Conference on Computer Vision, 2001, pp. 416-423.
H. Choi, R. G. Baraniuk. “Multiscale Image Segmentation Using Wavelet-Domain Hidden Markov Models“. IEEE Transactions on Image Processing, vol. 10, pp. 1309-1321, 2001.
H. D. Cheng, J. Li.” Fuzzy Homogeneity And Scale-Space Approach To Color Image Segmentation”. Pattern Recognition, vol.36, pp. 1545-1562, 2003.
H. D. Cheng, X. H. Jiang, J. L. Wang. ” Color Image Segmentation Based On Homogram Thresholding And Region Merging”. Pattern Recognition, vol.36, pp. 373-393, 2002.
J. C. Bezdek, “Pattern Recognition With Fuzzy Objective Function Algorithms,” in Plenum Press, New York, 1981.
J. Freixenet, X. Munoz, D. Raba, et al. “Yet another survey on image segmentation: region and boundary information integration,” in Proceedings of European Conference on Computer Vision, 2002, pp. 408-422.
J. Shi and J. Malik. “Normalized Cuts and Image Segmentation”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 888-905, 2000.
J.C. Dunn. “A Fuzzy Relative Of The ISODATA Process And Its Use In Detecting Compact, Well Separated Cluster”. Cybernetics, vol. 3, pp. 32–57, 1973.
K. S. Chuang, H. L. Tzeng, S. Chen, J. Wu, T. J. Chen. “Fuzzy C-Means Clustering With Spatial Information For Image Segmentation”. Computerized Medical Imaging and Graphics, vol. 30, pp. 9-15, 2006.
K. Y. Lin, J. H. Wu, L. H. Xu. “ A Survey On Color Image Segmentation Techniques”. Journal of Image And Graphics, vol. 10, pp. 1-10, 2005.
M. Meila. “Comparing clusterings - An axiomatic view,” in Proceedings of the 22nd International Conference on Machine Learning, 2005, pp. 577-584.
M. Mignotte. “Segmentation By Fusion Of Histogram-Based K-Means Clusters In Different Color Spaces”. IEEE Transactions on image processing, vol. 17,pp. 780-787, 2008.
P. Felzenszwalb and D. Huttenlocher, “Efficient Graph-Based Image Segmentation”. Int. J. Comput. Vis., vol. 59, pp. 167-181, 2004.
R. Unnikrishnan, C. Pantofaru, M. Hebert. “A measure for objective evaluation of image segmentation algorithms,” in Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 34-41.
R. Unnikrishnan, C. Pantofaru, M. Hebert. “Toward Objective Evaluation Of Image Segmentation Algorithms”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp. 929-944, 2007.
Y. Ma, H Derksen, W. Hong, and J. Wright. “Segmentation Of Multivariate Mixed Data Via Lossy Coding And Compression”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp. 1546-1562, 2007.
Mr. kun chen
The communist party of China - China
Professor Yan Ma
- China
Mr. Jun Liu
- China

View all special issues >>