Ten lectures on wavelets
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards a texture naming system: identifying relevant dimensions of texture
VIS '93 Proceedings of the 4th conference on Visualization '93
A new class of two-channel biorthogonal filter banks and waveletbases
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Texture classification using spectral histograms
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Texture Image Retrieval Based on Contourlet Transform and Active Perceptual Similarity Learning
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Contourlet-based texture retrieval using a mixture of generalized gaussian distributions
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
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This paper presents a texture image retrieval scheme based on contourlet transform. In this scheme, the generalized Gaussian distribution (GGD) parameters are used to represent the detail subband features obtained by contourlet transform. To obtain these parameters, an improved maximum likelihood (ML) parameter estimation method is proposed, in which a new initial estimation value is exploited and a modified iterative algorithm is used. Compared with existing features used for the texture image retrieval, the use of the GGD parameters to represent the contourlet detail subbands provides richer information to improve the retrieval accuracy. The proposed retrieval scheme is demonstrated on the VisTex database of 640 texture images. Experimental results show that, compared with the current ML estimation and texture retrieval method, the proposed scheme can give more accurate estimates of the GGD parameters, and it improves more effectively the average retrieval rate from 76.05% to 78.09% with comparable computational complexity.