Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance

We present a statistical view of the texture retrieval problem by combining the two related tasks, namely feature extraction (FE) and similarity measurement (SM), into a joint modeling and classification scheme. We show that using a consistent estimator of texture model parameters for the FE step followed by computing the Kullback-Leibler distance (KLD) between estimated models for the SM step is asymptotically optimal in term of retrieval error probability. The statistical scheme leads to a new wavelet-based texture retrieval method that is based on the accurate modeling of the marginal distribution of wavelet coefficients using generalized Gaussian density (GGD) and on the existence a closed form for the KLD between GGDs. The proposed method provides greater accuracy and flexibility in capturing texture information, while its simplified form has a close resemblance with the existing methods which uses energy distribution in the frequency domain to identify textures. Experimental results on a database of 640 texture images indicate that the new method significantly improves retrieval rates, e.g., from 65% to 77%, compared with traditional approaches, while it retains comparable levels of computational complexity.

[1]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[2]  M. Varanasi,et al.  Parametric generalized Gaussian density estimation , 1989 .

[3]  David Salesin,et al.  Fast multiresolution image querying , 1995, SIGGRAPH.

[4]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[5]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  B. S. Manjunath,et al.  NeTra: A toolbox for navigating large image databases , 1997, Multimedia Systems.

[8]  Nuno Vasconcelos,et al.  A unifying view of image similarity , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[9]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[10]  Alex Pentland,et al.  Photobook: Content-based manipulation of image databases , 1996, International Journal of Computer Vision.

[11]  Minh N. Do,et al.  Invariant Image Retrieval Using Wavelet Maxima Moment , 1999, VISUAL.

[12]  L. Devroye.  Non-Uniform Random Variate Generation , 1986 .

[13]  Shih-Fu Chang,et al.  Transform features for texture classification and discrimination in large image databases , 1994, Proceedings of 1st International Conference on Image Processing.

[14]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  M. Unser.  Local linear transforms for texture measurements , 1986 .

[16]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[17]  David S. Doermanny.  SPIE-Multimedia Storage and Archiving Systems , 2007 .

[18]  Paul A. Viola,et al.  Texture recognition using a non-parametric multi-scale statistical model , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[19]  Eero P. Simoncelli,et al.  Texture characterization via joint statistics of wavelet coefficient magnitudes , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[20]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[21]  Song-Chun Zhu,et al.  FRAME: filters, random fields, and minimax entropy towards a unified theory for texture modeling , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Paul Scheunders,et al.  Statistical texture characterization from discrete wavelet representations , 1999, IEEE Trans. Image Process..

[23]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[24]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[25]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[27]  A. Ravishankar Rao,et al.  Towards a texture naming system: Identifying relevant dimensions of texture , 1993, Vision Research.

[28]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[29]  Robert S. Shankland,et al.  Handbook of Mathematical Tables , 1963 .

[30]  James R. Bergen,et al.  Pyramid-based texture analysis/synthesis , 1995, Proceedings., International Conference on Image Processing.

[31]  Jan P. Allebach,et al.  Multiscale branch-and-bound image database search , 1997, Electronic Imaging.

[32]  Pierre Moulin,et al.  Analysis of Multiresolution Image Denoising Schemes Using Generalized Gaussian and Complexity Priors , 1999, IEEE Trans. Inf. Theory.

[33]  Andrew B. Lippman,et al.  Embedded mixture modeling for efficient probabilistic content-based indexing and retrieval , 1998, Other Conferences.

[34]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[35]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[36]  Kannan Ramchandran,et al.  Multimedia Analysis and Retrieval System (MARS) Project , 1996, Data Processing Clinic.

[37]  Martin Vetterli,et al.  Rotation-invariant texture retrieval using steerable wavelet-domain hidden Markov models , 2000, SPIE Optics + Photonics.

[38]  Song-Chun Zhu,et al.  Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling , 1998, International Journal of Computer Vision.

[39]  John P. Oakley,et al.  Storage and Retrieval for Image and Video Databases , 1993 .

[40]  Jianying Hu,et al.  Matching and retrieval based on the vocabulary and grammar of color patterns , 2000, IEEE Trans. Image Process..

[41]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[42]  Fang Liu,et al.  Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[44]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[45]  Alberto Leon-Garcia,et al.  Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video , 1995, IEEE Trans. Circuits Syst. Video Technol..

[46]  M. Do.  Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models , 2003, IEEE Signal Processing Letters.

[47]  Minh N. Do,et al.  Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models , 2002, IEEE Trans. Multim..