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Abstract:

This paper presents a texture descriptor based on wavelet frame transforms. At each position in the image, and for each resolution level, we consider both vertical and horizontal wavelet detail coefficients as the components of a bivariate random vector. The magnitudes and angles of these vectors are computed. At each level the empirical histogram of magnitudes is modeled by a Generalized Gamma distribution, and the empirical histogram of angles is modeled by a different version of the von Mises distribution that accounts for histograms with 2 modes. Each texture is characterized by few parameters. A new distance is presented (based on the Kullback-Leibler divergence) that allows giving relative importance to each model and to each resolution level. This distance is later conveniently adapted to provide for rotation invariance, by establishing equivalence classes over distributions of angles. Through a broad set of experiments on three different image databases, we demonstrate that our new descriptor and distance measure can be successfully applied in the context of texture retrieval. We compare our system to several relevant methods in this field in terms of retrieval performance and number of parameters used by each method. We also include some classification tests. In all the tests, we obtain superior retrieval rates for a set of fewer parameters involved. © 2014 Elsevier Ltd.

Registro:

Documento: Artículo
Título:A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval
Autor:De Ves, E.; Acevedo, D.; Ruedin, A.; Benavent, X.
Filiación:Universitat de València, Departament DInformàtica, Spain
Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Computación, Argentina
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Argentina
Palabras clave:Image retrieval; Rotation invariant; Statistical models; Texture descriptor; Wavelet frames; Equivalence classes; Graphic methods; Statistical methods; Wavelet analysis; Generalized gamma distribution; Kullback Leibler divergence; Retrieval performance; Rotation invariant; Statistical modeling; Texture descriptor; Von Mises distribution; Wavelet frame; Image retrieval
Año:2014
Volumen:47
Número:9
Página de inicio:2925
Página de fin:2939
DOI: http://dx.doi.org/10.1016/j.patcog.2014.03.004
Título revista:Pattern Recognition
Título revista abreviado:Pattern Recogn.
ISSN:00313203
CODEN:PTNRA
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00313203_v47_n9_p2925_DeVes

Referencias:

  • Di Lillo, A., Motta, G., Storer, J., Multiresolution rotation-invariant texture classification using feature extraction in the frequency domain and vector quantization (2008) Data Compression Conference, pp. 452-461
  • Sim, D.-G., Kim, H.-K., Park, R.-H., Fast texture description and retrieval of DCT-based compressed images (2001) Electron. Lett, 37 (1), pp. 18-19
  • Jain, A., Healey, G., A multiscale representation including opponent color features for texture recognition (1998) IEEE Trans. Image Process, 7 (1), pp. 124-128
  • Azencott, R., Wang, J., Younes, L., Texture classification using windowed Fourier filters (1997) IEEE Trans. Pattern Anal. Mach. Intell, 19 (2), pp. 148-153
  • Randen, J.H.T., Filtering for texture classification a comparative study (1999) IEEE Trans. Pattern Anal. Mach. Intell, 20, pp. 115-122
  • Kwitt, R., Uhl, A., Lightweight probabilistic texture retrieval (2010) IEEE Trans. Image Process, 19 (1), pp. 241-253
  • Vo, A., Oraintara, S., A study of relative phase in complex wavelet domain property, statistics and applications in texture image retrieval and segmentation (2010) Signal Process.: Image Commun, 25 (1), pp. 28-46
  • Do, M.N., Vetterli, M., Rotation invariant texture characterization and retrieval using steerable wavelet-domain Hidden Markov Models (2002) IEEE Trans. Multimed, 4 (4), pp. 517-527
  • Choy, S.K., Tong, C., Statistical wavelet subband characterization based on generalized Gamma density and its application in texture retrieval (2010) IEEE Trans. Image Process, 19 (2), pp. 281-289
  • Kokare, M., Biswas, P., Chatterji, B., Texture image retrieval using rotated wavelet filters (2007) Pattern Recognit. Lett, 28 (10), pp. 1240-1249
  • Buccigrossi, R., Simoncelli, E., Image compression via joint statistical characterization in the wavelet domain (1999) IEEE Trans. Signal Process, 8, pp. 1688-1701
  • Do, M.N., Vetterli, M., Wavelet-based texture retrieval using Generalized Gaussian Density and Kullback-Leibler distance (2002) IEEE Trans. Image Process, 11 (2), pp. 146-158
  • Wouwer, G.V., Scheunders, P., Dyck, D.V., Statistical texture characterization from discrete wavelet representations (1999) IEEE Trans. Image Process, 8, pp. 592-598
  • Allili, M., Wavelet modeling using finite mixtures of Generalized Gaussian Distributions application to texture discrimination and retrieval (2012) IEEE Trans. Image Process, 21 (4), pp. 1452-1464
  • De Ves, E., Ruedin, A., Acevedo, D., Benavent, X., Seijas, L., A new wavelet-based texture descriptor for image retrieval (2007) Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, 4673, pp. 895-902
  • De Ves, E., Benavent, X., Ruedin, A., Acevedo, D., Seijas, L., Wavelet-based texture retrieval modeling the magnitudes of wavelet detail coefficients with a generalized Gamma distribution (2010) IEEE International Conference on Pattern Recognition (ICPR), pp. 221-221
  • Tzagkarakis, G., Beferull-Lozano, B., Tsakalides, P., Rotation-invariant texture retrieval with Gaussianized steerable pyramids (2006) IEEE Trans. Image Process, 15, pp. 413-416
  • Kwitt, R., Meerwald, P., Uhl, A., Efficient texture image retrieval using copulas in a Bayesian framework (2011) IEEE Trans. Image Process, 20 (7), pp. 2063-2077
  • Vo, A., Oraintara, S., Nguyen, T., Using phase and magnitude information of the complex directional filter bank for texture image retrieval (2007) IEEE International Conference on Image Processing (ICIP 2007), 4, pp. 61-64
  • Peron, M.-C., Da Costa, J.-P., Stitou, Y., Germain, C., Berthoumieu, Y., Joint linear-circular stochastic models for texture classification (2009) IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2009), pp. 1073-1073
  • Jafari-Khouzani, K., Soltanian-Zadeh, H., Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms (2005) IEEE Trans. Image Process, 14 (JUNE 6), pp. 783-795
  • Lowe, D.G., Distinctive image features from scale-invariant keypoints (2004) Int. J. Comput. Vis, 60 (2), pp. 91-110
  • Sikora, T., The mpeg-7 visual standard for content description - An overview (2001) IEEE Trans. Circuits Syst. Video Technol, 11 (6), pp. 696-702
  • Deng, H., Clausi, D., Gaussian MRF rotation-invariant features for image classification (2004) IEEE Trans. Pattern Anal. Mach. Intell, 26 (7), pp. 951-955
  • Ojala, T., Pietikainen, M., Maenpaa, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns (2002) IEEE Trans. Pattern Anal. Mach. Intell, 24 (7), pp. 971-987
  • Guo, Z., Li, Q., You, J., Zhang, D., Liu, W., Local directional derivative pattern for rotation invariant texture classification (2012) Neural Comput. Appl, 21, pp. 1893-1904
  • Mallat, S., (1999) A Wavelet Tour of Signal Processing, , Academic Press, San Diego, USA
  • Daubechies, I., (1992) Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, , Philadelphia, USA
  • Cohen, A., Daubechies, I., Feauveau, J., Biorthogonal bases of compactly supported wavelets (1992) Commun. Pure Appl. Math, 45, pp. 485-560
  • Song, K.-S., Globally convergent algorithms for estimating Generalized Gamma Distributions in fast signal and image processing (2008) IEEE Trans. IP, 17 (8), pp. 1233-1250
  • Gatto, R., Jammalamadaka, S., The generalized von Mises distribution (2007) Stat. Methodol, 4, pp. 341-353
  • Nocedal, J., Wright, S.J., (2006) Numerical Optimization, , Springer, New York, USA
  • Vision Texture Database, , http://www.vismod.media.mit.edu/vismod/imagery/VisionTexture, MIT Vision Modeling Group
  • The USC-SIPI Image Database, , http://www.sipi.usc.edu/database/database.php, University of Southern California Signal and Image Processing Institute
  • UIUC Texture Database, , http://www-cvr.ai.uiuc.edu/poncegrp/data/index.html
  • UMD Texture Database, , http://www.cfar.umd.edu/~fer/High-resolution-data-base/hr_database.htm
  • Varma, M., Garg, R., Locally invariant fractal features for statistical texture classification (2007) IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1-8
  • Xu, Y., Ji, H., Fermuller, C., Viewpoint invariant texture description using fractal analysis (2009) Int. J. Comput. Vis, 83 (1), pp. 85-100
  • Lazebnik, S., Schmid, C., Ponce, J., A sparse texture representation using local affine regions (2005) IEEE Trans. Pattern Anal. Mach. Intell, 27 (8), pp. 1265-1278. , doi:10.1109/TPAMI.2005.151, doi
  • Xu, Y., Huang, S.B., Ji, H., Fermuller, C., Combining powerful local and global statistics for texture description (2009) IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 573-580
  • Ji, H., Yang, X., Ling, H., Xu, Y., Wavelet domain multifractal analysis for static and dynamic texture classification (2013) IEEE Trans. Image Process, 22 (1), pp. 286-299

Citas:

---------- APA ----------
De Ves, E., Acevedo, D., Ruedin, A. & Benavent, X. (2014) . A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval. Pattern Recognition, 47(9), 2925-2939.
http://dx.doi.org/10.1016/j.patcog.2014.03.004
---------- CHICAGO ----------
De Ves, E., Acevedo, D., Ruedin, A., Benavent, X. "A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval" . Pattern Recognition 47, no. 9 (2014) : 2925-2939.
http://dx.doi.org/10.1016/j.patcog.2014.03.004
---------- MLA ----------
De Ves, E., Acevedo, D., Ruedin, A., Benavent, X. "A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval" . Pattern Recognition, vol. 47, no. 9, 2014, pp. 2925-2939.
http://dx.doi.org/10.1016/j.patcog.2014.03.004
---------- VANCOUVER ----------
De Ves, E., Acevedo, D., Ruedin, A., Benavent, X. A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval. Pattern Recogn. 2014;47(9):2925-2939.
http://dx.doi.org/10.1016/j.patcog.2014.03.004