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Neural Computing and Applications

, Volume 21, Issue 8, pp 1893–1904 | Cite as

Local directional derivative pattern for rotation invariant texture classification

  • Zhenhua Guo
  • Qin Li
  • Jane You
  • David Zhang
  • Wenhuang Liu
ICIC2010

Abstract

Local binary pattern (LBP) is a simple and efficient operator to describe local image pattern. It could be regarded as a binary representation of 1st order derivative between the central and its neighbors. Based on LBP definition, in this paper, a framework of local directional derivative pattern (LDDP) is proposed which could represent high order directional derivative feature, and LBP is a special case of LDDP. Under the proposed framework, like traditional LBP, rotation invariance could be easily defined. As different order derivative information contains complementary features, better recognition accuracy could be achieved by combining different order LDDPs which is validated by two large public texture databases, Outex and CUReT.

Keywords

Texture classification Local binary pattern (LBP) Rotation invariance Local directional derivative pattern (LDDP) 

Notes

Acknowledgments

The authors wish to thank the anonymous reviewers for the constructive advice on the revision of the manuscript. The authors sincerely thank MVG and VGG for sharing the source codes of LBP and VZ_MR8. The funding support from Hong Kong Government under its GRF scheme (5341/08E and 5366/09E), the research grant from the Hong Kong Polytechnic University (1-ZV5U), and the NSFC (Nos. 60803090 and 61020106004) are greatly appreciated.

References

  1. 1.
    Tuceryan M, Jain AK (1993) Texture analysis, handbook of pattern recognition and computer vision, chap 2. In: Chen CH, Pau LF, Wang PSP (eds). World Scientific Publishing Co., pp 235–276Google Scholar
  2. 2.
    Cohen FS, Fan Z, Attali S (1991) Automated inspection of textile fabrics using textural models. IEEE Trans Pattern Anal Mach Intell 13(8):803–808CrossRefGoogle Scholar
  3. 3.
    Anys H, He DC (1995) Evaluation of textural and multipolarization radar features for crop classification. IEEE Trans Geosci Remote Sens 33(5):1170–1181CrossRefGoogle Scholar
  4. 4.
    Ji Q, Engel J, Craine E (2000) Texture analysis for classification of cervix lesions. IEEE Trans Med Imaging 19(11):1144–1149CrossRefGoogle Scholar
  5. 5.
    Haralik RM, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621CrossRefGoogle Scholar
  6. 6.
    Bovik AC, Clark M, Geisler WS (1990) Multichannel texture analysis using localized spatial filters. IEEE Trans Pattern Anal Mach Intell 12(1):55–73CrossRefGoogle Scholar
  7. 7.
    Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842CrossRefGoogle Scholar
  8. 8.
    Chang T, Kuo CCJ (1993) Texture analysis and classification with tree-structured wavelet transform. IEEE Trans Image Process 2(4):429–441CrossRefGoogle Scholar
  9. 9.
    Laine A, Fan J (1993) Texture classification by wavelet packet signatures. IEEE Trans Pattern Anal Mach Intell 15(11):1186–1191CrossRefGoogle Scholar
  10. 10.
    Unser M (1995) Texture classification and segmentation using wavelet frames. IEEE Trans Image Process 4(11):1549–1560CrossRefGoogle Scholar
  11. 11.
    Kashyap RL, Khotanzed A (1986) A model-based method for rotation invariant texture classification. IEEE Trans Pattern Anal Mach Intell 8(4):472–481CrossRefGoogle Scholar
  12. 12.
    Mao J, Jain AK (1992) Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recogn 25(2):173–188CrossRefGoogle Scholar
  13. 13.
    Chen JL, Kundu A (1994) Rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden Markov model. IEEE Trans Pattern Anal Mach Intell 16(2):208–214CrossRefGoogle Scholar
  14. 14.
    Wu WR, Wei SC (1996) Rotation and gray-scale transform-invariant texture classification using spiral resampling, subband decomposition, and hidden Markov model. IEEE Trans Image Process 5(10):1423–1434CrossRefGoogle Scholar
  15. 15.
    Porter R, Canagarajah N (1997) Robust rotation-invariant texture classification: wavelet, Gabor, and GMRF based schemes. IEE Proc Vis Image Signal Process 144(3):180–188CrossRefGoogle Scholar
  16. 16.
    Arof H, Deravi F (1998) Circular neighbourhood and 1-D DFT features for texture classification and segmentation. IEE Proc Vis Image Signal Process 145(3):167–172CrossRefGoogle Scholar
  17. 17.
    Ojala T, Pietikäinen M, Mäenpää TT (2002) Multiresolution gray-scale and rotation invariant texture classification with Local Binary Pattern. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  18. 18.
    Varma M, Zisserman A (2005) A statistical approach to texture classification from single images. Int J Comput Vis 62(1–2):61–81Google Scholar
  19. 19.
    Varma M, Zisserrman A (2009) A statistical approach to material classification using image patch examplars. IEEE Trans Pattern Anal Mach Intell 31(11):2032–2047CrossRefGoogle Scholar
  20. 20.
    Lazebnik S, Schmid C, Ponce J (2005) A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell 27(8):1265–1278CrossRefGoogle Scholar
  21. 21.
    Xu Y, Ji H, Fermuller C (2009) Viewpoint invariant texture description using fractal analysis. Int J Comput Vis 83(1):85–100CrossRefGoogle Scholar
  22. 22.
    Mellor M, Hong B, Brady M (2008) Locally rotation, contrast, and scale invariant descriptors for texture analysis. IEEE Trans Pattern Anal Mach Intell 30(1):52–61CrossRefGoogle Scholar
  23. 23.
    Ojala T, Mäenpää T, Pietikäinen M, Viertola J, Kyllönen J, Huovinen S (2002) Outex—new framework for empirical evaluation of texture analysis algorithm. In: International conference on pattern recognition. pp 701–706Google Scholar
  24. 24.
    Ahonen T, Hadid A, Pietikäinen M (2006) Face recognition with Local Binary Patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041CrossRefGoogle Scholar
  25. 25.
    Zhang B, Zhang L, Zhang D, Shen L (2010) Directional binary code with application to PolyU near-infrared face database. Pattern Recogn Lett 31(14):2337–2344CrossRefGoogle Scholar
  26. 26.
    Zhao G, Pietikäinen M (2007) Dynamic texture recognition using Local Binary Patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 27(6):915–928CrossRefGoogle Scholar
  27. 27.
    Huang X, Li SZ, Wang Y (2004) Shape localization based on statistical method using extended local binary pattern. In: Proceedings of the international conference on image and graphics. pp 184–187Google Scholar
  28. 28.
    Heikkilä M, Pietikäinen M, Schmid C (2009) Description of interest regions with local binary patterns. Pattern Recogn 42(3):425–436zbMATHCrossRefGoogle Scholar
  29. 29.
    Liao S, Law MWK, Chung ACS (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118CrossRefGoogle Scholar
  30. 30.
    Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650MathSciNetCrossRefGoogle Scholar
  31. 31.
    Zhang B, Gao Y, Zhao S, Liu J (2010) Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans Image Process 19(2):533–544MathSciNetCrossRefGoogle Scholar
  32. 32.
    Hyvärinen A (1999) Survey on independent component analysis. Neural Comput Surv 2:94–128Google Scholar
  33. 33.
    Dana KJ, van Ginneken B, Nayar SK, Koenderink JJ (1999) Reflectance and texture of real world surfaces. ACM Trans Graph 18(1):1–34CrossRefGoogle Scholar
  34. 34.
    Varma M, Zisserman A (2004) Unifying statistical texture classification framework. Image Vis Comput 22(14):1175–1183CrossRefGoogle Scholar
  35. 35.
    Rubner Y, Puzicha J, Tomasi C, Buhmann JM (2001) Empirical evaluation of dissimilarity measures for color and texture. Comput Vis Image Underst 84(1):25–43zbMATHCrossRefGoogle Scholar
  36. 36.
    Woods K, Kegelmeyer WP Jr, Bowyer K (1997) Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Anal Mach Intell 19(4):405–410CrossRefGoogle Scholar
  37. 37.
    Kittler J, Hatef M, Duin RP, Matas JG (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226–239CrossRefGoogle Scholar
  38. 38.
    Xie J, Zhang L, You J, Zhang D (2010) Texture classification via patch-based sparse texton learning. In: international conference on image processing. pp 2737–2740Google Scholar
  39. 39.
    Pietikäinen M, Nurmela T, Mäenpää T, Turtinen M (2004) View-based recognition of real-world textures. Pattern Recogn 37(2):313–323zbMATHCrossRefGoogle Scholar
  40. 40.
    Xu Y, Song F (2008) Feature extraction based on a linear separability criterion. Int J Innov Comput Inf Control 4(4):857–865MathSciNetGoogle Scholar
  41. 41.
    Xu Y, Yang J, Jin Z (2004) A novel method for Fisher discriminant analysis. Pattern Recogn 37(2):381–384zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Zhenhua Guo
    • 1
  • Qin Li
    • 2
  • Jane You
    • 3
  • David Zhang
    • 3
  • Wenhuang Liu
    • 1
  1. 1.Graduate School at ShenzhenTsinghua UniversityShenzhenChina
  2. 2.College of Optoelectronic EngineeringShenzhen UniversityShenzhenChina
  3. 3.Department of ComputingThe Hong Kong Polytechnic UniversityHung HomHong Kong

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