Rotation and Scale Invariant Texture Classification Using Wavelet Transform and LBP Operator


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Local Binary Patterns (LBP) is a local approach widely used in the field of texture analysis. Generally, the LBP algorithm is applied on the original texture. Our contribution, as presented in this paper, will be to apply this algorithm on the sub bands resulting from the wavelet transform. This allows characterising texture on various resolution levels. As training bases, we used a set of 30 elements extracted from the Brodatz album and a set of 40 elements extracted from the Vistex album. To test the invariance of the proposed method, several tests have been carried out on textures with rotation changes or scale changes, and many parameters have been tested including the radius of the LBP, the distance measure and the wavelet's nature. These results demonstrate the effectiveness of our characterization method in texture image classification experiments.
Copyright © 2013 Praise Worthy Prize - All rights reserved.

Keywords


LBP; Wavelet Transform; Texture; Classifications

Full Text:

PDF


References


Z. Guo, L. Zhang and D. Zhang, Rotation invariant texture classification using LBP variance (LBPV) with global matching, Pattern Recognition 43, 2010, pp.706–719.

S. Liao, Max W. K. Law and Albert C. S. Chung, Dominant Local Binary Patterns for Texture Classification, IEEE Transactions on image processing, vol. 18, no. 5, May 2009.

W. Zhang, S. Shan, X. Chen and W. Gao, Local Gabor Binary Patterns Based on Kullback–Leibler Divergence for Partially Occluded Face Recognition, IEEE Signal processing letters, vol. 14, no. 11, November 2007.

Y. Wang, X. Wei and S. Xiao, LBP texture analysis Based on the Local Adaptive Niblack Algorithm, IEEE Congress on Image and Signal Processing, 2008.

Y. Kang, O. Hasegawa and H. Nagahashi, Texture structure classification and depth estimation using multi-scale local autocorrelation features, IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’03), 2003.

R. Jobanputra and D. A. Clausi, Preserving boundaries for image texture segmentation using grey level co-occurring probabilities, Pattern Recognition 39, 2006, pp. 234–245.

D. A. Clausi and H. Deng, Design-based texture feature fusion using gabor filters and co-occurrence probabilities, IEEE Transaction on Image Processing 14. no. 7, 2005, pp. 925–936.

X. Tang, Texture information in run-length matrices, IEEE Transactions on Image Processing 7, no. 11, 1998, pp. 1602–1609.

M. Bartels, H. Wei and D. C. Mason, Wavelet packets and co-occurrence matrices for texture-based image segmentation, IEEE International Conference on Advanced Video and Signal-based Surveillance, vol. 1, 2005, pp. 428–433.

T. Maenpää and M. Pietikäinen, Classification with color and texture : jointly or separately ?, Pattern Recognition 37, no. 1, 2004, pp. 1629–1640.

T. Ahonen and M. Pietikäinen, Image description using joint distribution of filter bank responses, Pattern Recognition Letters 30, no. 4, 2009, pp. 368–376.

M. Heikkila, M. Pietikäinen and C. Schmid, Description of interest regions with local binary patterns, Pattern Recognition 42, no. 3, 2009, pp. 425–436.

M. Tuceryan and A. K. Jain, Texture segmentation using voronoï polygons, IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-12, 1990, pp. 211–216.

F. Tomita and S. Tsuji, Computer analysis of visual textures, Edition Kluwer Academic, 1990.

Y. Chen and E. Dougherty, Grey-scale morphological granulometric texture classification, Optical Engineering 33, no. 8, 1994, pp. 2713–2722.

Y. Chen, M. Nixon and D. Thomas, Texture classification using statistical geometrical features, Proceedings of IEEE International Conference on Image Processing (ICIP), vol. 1, 1994, pp. 446–450.

G. Rellier, X. Descombes, F. Falzon and J. Zerubia, Analyse de texture hyperspectrale par modélisation markovienne, Rapport technique, Unité de recherche INRIA Sophia Antipolis, 2002.

S. M. Schweizer and J. Moura, Hyperspectral imagery : Clutter adaptation in anomaly detection, IEEE Transaction on Information Theory 46, no. 5, 2000, pp. 1855–1871.

G. Hazel, Multivariate gaussian mrf for multispectral scene segmentation and anomaly detection, IEEE Transaction on Geoscience and Remote Sensing 38, no. 3, 2000, pp. 1199–1211.

A. Lorette, X. Descombes and J. Zerubia, Urban areas extraction based on texture analysis through a markovian modeling, International Journal of Computer Vision 36, no. 3, 2000, pp. 219–234.

H. G. Bu, J. Wang and X. B. Huang, Fabric defect detection based on multiple fractal features and support vector data description, Engineering Applications of Artificial Intelligence 22, no. 2, 2009, pp. 224–235.

E. Lespessailles, C. C. B. N. and C. L. Benhamou, Imaging techniques for evaluating bone microarchitecture, Joint Bone Spine 73, no. 3,2006, pp. 254–261.

A. R. Backes and O. M. Bruno, A new approach to estimate fractal dimension of texture images, Proceedings of The 3rd international conference on Image and Signal Processing (ICIP), vol. 1, 2008, pp. 136–143.

Mäenpää, Topi, The local binary pattern approach to texture analysis – extensions and applications, Infotech Oulu and Department of Electrical and Information Engineering, University of Oulu, P.O.Box 4500, FIN-90014 University of Oulu, Finland Oulu, Finland 2003.

H. Mahersia and K. Hamrouni, Rotation and scale invariant texture classification using log-polar and ridgelet transforms, Machine GRAPHICS and VISION, vol. 18, no. 2, 2009, pp. 215–232.

C.-M. Pun and M.-C. Lee, Log-polar wavelet energy signatures for rotation and scale invariant texture classification, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, 2003, pp. 590–603.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize