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Fast Texture Image Segmentation Algorithm Based on Dual-tree Complex Wavelet Transform

Yanli Hou


A fast texture image segmentation algorithm based on dual-tree complex wavelet transform is studied in this paper. Firstly, texture features are extracted on the basic of analyzing the histogram of the magnitude sub-bands, which is produced by the dual-tree complex wavelet transform six high-frequency sub-bands. Secondly, the k-means clustering algorithm is directly applied to the texture segmentation, but the time needed in each iteration step for the k-means algorithm is not only directly proportional to the data number and the class number, but also the dimension of the feature. When the feature dimension is high, the convergence speed of the algorithm is slow. So, the kd-tree data structure is introduced to modify the traditional k-means clustering algorithm. At last, simulations are performed on the presented algorithm, and the simulation result shows that the presented algorithm not only has good accuracy but also has good performance in-time.


texture segmentation, dual-tree complex wavelet transform, k-means, kd-tree.

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