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Texture Based Image Clustering Using Wavelets

P. Jeyanthi, Dr.V. Jawahar SenthilKumar

Abstract


Clustering is traditionally viewed as an unsupervised method for data analysis. The primary objective of cluster analysis is to partition a given data set into homogeneous clusters. In this paper, we present a novel algorithm for performing texture based clustering using wavelets. The approximation band of image Discrete Wavelet Transform is considered forsegmentation which contains significant information of the input image. The Histogram based algorithm is used to obtain the number of regions and the initial parameters like mean, variance and mixing factor. The centroides are calculated and perform the clustering using k means .It is observed that the proposed method is computationally efficient than the k means algorithm and improved k means algorithm.


Keywords


Cluster, Histogram, Segmentation, Wavelet

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References


Yu-fang zhang, jia-li mao, zhong-yang xiong“Anefficientclustering Algorithm”, proceedings of the second international conference on machine learning and cybernetics, xi’an, 2- november 2003proceedings of the second international conference on machine learning and

cybernetics, xi’an, 2-5 november 2003.

Fang yuan', zeng-hui meng , hong-xia zhangzs3, chun-ru dong “a new algorithm to get the initial centroids “, proceedings of the third international conference on machine laming and cybernetics, shanghai, 26-29 august 2004.

S. R. Aboud Neta, L. V. Dutra, G. J. Erthal1, “Limiarização automática em histogramas multimodais”. Proceeedings of the 7th Brazilian Conference on Dynamics, Control and Applications, FCT – Unesp de Presidente Prudente, May, 2008.

YoungeunAn,JungukBaek etal,“Classification of Feature set using Kmeans Clustering from Histogram Refinement method”,IEEE 2008.

Donn Morrison, Stephane Marchand –Maillet, Eric Bruno ,“ Semantic clustering of images using patterns of relevance feedback”, IEEE 2008.

Anirban Mukhopadhyay, Sanghamitra Bandyopadhyay, Senior Member,IEEE,and Ujjwal Maulik, Senior Member, IEEE,”Combining Multiobjective Fuzzy Clustering and Probabilistic ANNClassifier for UnsupervisedPattern Classification: Application to Satellite Image

Segmentation”, 2008 IEEE congress on evolutionary computation (CEC 2008)

Mavrinac, “Competitive Learning Techniques for Color Image Segmentation,” Proceedings of the Machine Learning and Computer Vision, vol. 88, no. 590, pp. 33-37, April 2007.

B. Sumengen and B. S. Manjunath, “Multi-Scale Edge Detection and Image Segmentation,” Proceedings of European Signal Processing Conference, September 2005.

Y. Wu, X. Yang and K. L. Chan, “Unsupervised Color Image Segmentation based on Gaussian Mixture Models,” Proceedings of Fourth International Joint Conference on Information, Communications and Signal Processing, vol.1, no. 15, pp. 541-544, December 2003

J. Bruce, T. Balch and M. Veloso, “Fast and Inexpensive Color Image Segmentation for Interactive Robots,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, vol. 3, pp.2061-2066, 2000.


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