LBP Histogram Selection based on Sparse Representation for Color Texture Classification

Vinh Truong Hoang, Alice Porebski, Nicolas Vandenbroucke, Denis Hamad

2017

Abstract

In computer vision fields, LBP histogram selection techniques are mainly applied to reduce the dimension of color texture space in order to increase the classification performances. This paper proposes a new histogram selection score based on Jeffrey distance and sparse similarity matrix obtained by sparse representation. Experimental results on three benchmark texture databases show that the proposed method improves the performance of color texture classification represented in different color spaces.

References

  1. Asada, N. and Matsuyama, T. (1992). Color image analysis by varying camera aperture. In Pattern Recognition, 1992. Vol.I. Conference A: Computer Vision and Applications, Proceedings., 11th IAPR International Conference on, pages 466-469.
  2. Backes, A. R., Casanova, D., and Bruno, O. M. (2012). Color texture analysis based on fractal descriptors. Pattern Recognition, 45(5):1984-1992.
  3. Cha, S.-H. and Srihari, S. N. (2002). distance between histograms. 35(6):1355-1370.
  4. El Maliani, A. D., El Hassouni, M., Berthoumieu, Y., and Aboutajdine, D. (2014). Color texture classification method based on a statistical multi-model and geodesic distance. Journal of Visual Communication and Image Representation, 25(7):1717-1725.
  5. Florindo, J. and Bruno, O. (2016). Texture analysis by fractal descriptors over the wavelet domain using a best basis decomposition. Physica A: Statistical Mechanics and its Applications, 444:415-427.
  6. Guo, J.-M., Prasetyo, H., Lee, H., and Yao, C.-C. (2016). Image retrieval using indexed histogram of Void-andCluster Block Truncation Coding. Signal Processing, 123:143-156.
  7. Guo, Y., Zhao, G., and Pietikäinen, M. (2012). Discriminative features for texture description. Pattern Recognition, 45(10):3834-3843.
  8. Kalakech, M., Porebski, A., Vandenbroucke, N., and Hamad, D. (2015). A new LBP histogram selection score for color texture classification. In Image Processing Theory, Tools and Applications (IPTA), 2015 International Conference on, pages 242-247.
  9. Lakmann, R. (1998). Barktex benchmark database of color textured images.
  10. Ledoux, A., Losson, O., and Macaire, L. (2016). Color local binary patterns: compact descriptors for texture classification. Journal of Electronic Imaging, 25(6):061404.
  11. Liu, M. and Zhang, D. (2014). Sparsity score: a novel graph-preserving feature selection method. International Journal of Pattern Recognition and Artificial Intelligence, 28(04):1450009.
  12. Martínez, R. A., Richard, N., and Fernandez, C. (2015). Alternative to colour feature classification using colour contrast ocurrence matrix. In The International Conference on Quality Control by Artificial Vision 2015, pages 953405-953405. International Society for Optics and Photonics.
  13. Mehta, R. and Egiazarian, K. (2016). Dominant Rotated Local Binary Patterns (DRLBP) for texture classification. Pattern Recognition Letters, 71:16-22.
  14. Mäenpää, T. and Pietikäinen, M. (2004). Classification with color and texture: jointly or separately? Pattern Recognition, 37(8):1629-1640.
  15. Ojala, T., Maenpaa, T., Pietikainen, M., Viertola, J., Kyllonen, J., and Huovinen, S. (2002a). Outex - new framework for empirical evaluation of texture analysis algorithms. In Pattern Recognition, 2002. Proceedings. 16th International Conference on, volume 1, pages 701-706 vol.1.
  16. Ojala, T., Pietikäinen, M., and Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1):51 - 59.
  17. Ojala, T., Pietikäinen, M., and Mäenpää, T. (2001). A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification. In Proceedings of the Second International Conference on Advances in Pattern Recognition, ICAPR 7801, pages 397-406, London, UK, UK. Springer-Verlag.
  18. Ojala, T., Pietikäinen, M., and Mäenpää, T. (2002b). Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Anal. Mach. Intell., 24(7):971-987.
  19. Oliveira, M. W. d. S., da Silva, N. R., Manzanera, A., and Bruno, O. M. (2015). Feature extraction on local jet space for texture classification. Physica A: Statistical Mechanics and its Applications, 439:160-170.
  20. Pietikäinen, M., Mäenpää, T., and Viertola, J. (2002). Color texture classification with color histograms and local binary patterns. In Workshop on Texture Analysis in Machine Vision, pages 109-112.
  21. Porebski, A., Vandenbroucke, N., and Hamad, D. (2013a). LBP histogram selection for supervised color texture classification. In ICIP, pages 3239-3243.
  22. Porebski, A., Vandenbroucke, N., and Macaire, L. (2013b). Supervised texture classification: color space or texture feature selection? Pattern Analysis and Applications, 16(1):1-18.
  23. Porebski, A., Vandenbroucke, N., Macaire, L., and Hamad, D. (2014). A new benchmark image test suite for evaluating colour texture classification schemes. Multimedia Tools and Applications, 70(1):543-556.
  24. Qazi, I.-U.-H., Alata, O., Burie, J.-C., Moussa, A., and Fernandez-Maloigne, C. (2011). Choice of a pertinent color space for color texture characterization using parametric spectral analysis. Pattern Recognition, 44(1):16-31.
  25. Qiao, L., Chen, S., and Tan, X. (2010). Sparsity preserving projections with applications to face recognition. Pattern Recognition, 43(1):331-341.
  26. Ren, J., Jiang, X., and Yuan, J. (2015). Learning LBP structure by maximizing the conditional mutual information. Pattern Recognition, 48(10):3180-3190.
  27. Xu, J., Yang, G., Man, H., and He, H. (2013). L1 graph based on sparse coding for feature selection. In Advances in Neural Networks-ISNN 2013, pages 594- 601. Springer.
  28. Zhang, Q. and Xu, Y. (2015). Block-based selection random forest for texture classification using multifractal spectrum feature. Neural Computing and Applications.
  29. Zhou, G., Lu, Z., and Peng, Y. (2013a). L1-graph construction using structured sparsity. Neurocomputing, 120(0):441 - 452.
  30. Zhou, S.-R., Yin, J.-P., and Zhang, J.-M. (2013b). Local binary pattern (LBP) and local phase quantization (LBQ) based on Gabor filter for face representation. Neurocomputing, 116:260-264.
  31. Zhu, X., Wu, X., Ding, W., and Zhang, S. (2013). Feature selection by joint graph sparse coding. In SDM, pages 803-811. SIAM.
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Paper Citation


in Harvard Style

Hoang V., Porebski A., Vandenbroucke N. and Hamad D. (2017). LBP Histogram Selection based on Sparse Representation for Color Texture Classification . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 476-483. DOI: 10.5220/0006128204760483


in Bibtex Style

@conference{visapp17,
author={Vinh Truong Hoang and Alice Porebski and Nicolas Vandenbroucke and Denis Hamad},
title={LBP Histogram Selection based on Sparse Representation for Color Texture Classification},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={476-483},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006128204760483},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - LBP Histogram Selection based on Sparse Representation for Color Texture Classification
SN - 978-989-758-225-7
AU - Hoang V.
AU - Porebski A.
AU - Vandenbroucke N.
AU - Hamad D.
PY - 2017
SP - 476
EP - 483
DO - 10.5220/0006128204760483