1.College of Computer Science and Technology, Zhejiang University, Hangzhou 310027 2.College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018
Abstract As an important feature, texture plays a critical role in image retrieval. A clustering method is proposed based on image texture. Rotated complex wavelet (RCW) and dual-tree complex wavelet transform (DT-CWT) are used to decompose image into high frequency coefficients in twelve directions. The histogram signatures can be computed from each high frequency sub-band. Combined with other features, those signatures are employed to compute the similarity between data points for the improved spectral clustering to reduce dimensionality. In the final step, k-means is applied on the dimensionality-reduced data to get the clustering result. The proposed histogram signature for RCW and DT-CWT decomposition can capture the high frequency information in each direction effectively. In addition, an adaptive approach is proposed to compute the similarity between data points in spectral clustering. The experimental results show the proposed method outperforms the traditional methods remarkably.
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2009, Vol. 22 
