Yanli Hou
School of Computer and Information Technology, Shangqiu Normal University, China
ABSTRACT
This study presents an efficient method for texture image segmentation based on dual-tree complex wavelet transform (DT-CWT)and improved Fuzzy C-means (FCM) algorithm. The procedure toward complete segmentation consists of two steps: texture feature extraction and feature classification. Firstly, texture feature is extracted in dual-tree complex wavelet domain for its shift invariance and more direction selectivity, we choose mean and variance of six high-frequency magnitude sub-bands as the texture features. Secondly, the fuzzy c-means algorithm is applied to the feature classification, but due to the random selectivity of initial clustering center, the clustering seeds may be too close which makes the FCM algorithm easily fall into local minimum, aiming at the problem, a new method based on maximun distance is proposed. In addition, to improve the membership function, the fuzzy connectedness of samples in the same cluster is proposed. Compared with the FCM algorithm, the experimental results show that the presented algorithm is more effective in texture image segmentation. At the same time, the presented algorithm is well applied to the segmentation of aero-image corrupted by noise.
PDF References Citation
How to cite this article
Yanli Hou, 2013. Application of Improved Fuzzy C-means Algorithm to Texture Image Segmentation. Information Technology Journal, 12: 6379-6384.
DOI: 10.3923/itj.2013.6379.6384
URL: https://scialert.net/abstract/?doi=itj.2013.6379.6384
DOI: 10.3923/itj.2013.6379.6384
URL: https://scialert.net/abstract/?doi=itj.2013.6379.6384
REFERENCES
- Arivazhagan, S. and L. Ganesan, 2003. Texture classification using wavelet transform. Pattern Recog. Lett., 24: 1513-1521.
Direct Link - Horng, M.H., 2011. Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst. Appl., 38: 13785-13791.
CrossRefDirect Link - Kang, C.C., W.J. Wang and C.H. Kang, 2012. Image segmentation with complicated background by using seeded region growing. AEU-Int. J. Electron. Commun., 66: 767-771.
CrossRef - Kingsbury, N.G., 2001. Complex wavelets for shift invariant analysis and filtering of signals. J. Applied Comput. Harmonic Anal., 10: 234-253.
CrossRefDirect Link - Mallat, S.G., 1989. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell., 11: 674-693.
CrossRefDirect Link - Phadke, A.C., S.R. Wadke and P.R. Rege, 2009. Region based segmentation using dual tree complex wavelet transform and rotated complex wavelet filter. Proceedings of the Annual IEEE India Conference, December 18-20, 2009, Gujarat, pp: 1-4.
CrossRef - Celik, T. and T. Tjahjadi, 2009. Multiscale texture classification using dual-tree complex wavelet transform. Pattern Recog. Lett., 3: 331-339.
CrossRefDirect Link - Udupa, J.K. and S. Samarasekera, 1996. Fuzzy connectedness and object definition: Theory, algorithm and applications in image segmentation. Graphical Models Image Proces., 58: 246-261.
CrossRef - Wang, X.L. and L.C. Jiao, 2007. Texture image segmentation based on DT-CWT and MRF model. Chin. J. Comput. Sci., 1: 187-190.
Direct Link