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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
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KEYWORDS
Color Image Segmentation, Histogram, SFCM, Fusion
ABSTRACT
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.
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Mr. kun chen
The communist party of China - China
kun_1949301@126.com
Professor Yan Ma
- China
Mr. Jun Liu
- China