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Neural Computing and Applications

, Volume 22, Issue 3–4, pp 793–802 | Cite as

Multi-ring local binary patterns for rotation invariant texture classification

  • Yonggang He
  • Nong Sang
Original Article

Abstract

The local binary pattern (LBP) approach has been widely used in texture description. In this paper, we build a new framework to extract the binary patterns and propose a robust texture descriptor: multi-ring local binary pattern (MrLBP). The MrLBP algorithm creates patterns from several ringed areas and mainly contains two parts. One is the extra-ring local binary pattern operator that gets patterns from the mean values of different ringed areas. The other is the intra-ring local binary pattern operator that obtains patterns by counting the majority of binary values in every single ringed area. Moreover, the binary formation of each part of the MrLBP is obtained from two different aspects. The MrLBP method not only considers the binary relationship among pixels in a local region, but also focuses on the relationship between pixels in a local region and the whole image. This is a little different from the conventional LBP methods that only get the binary formation from the local gray scales differences. The experimental results on two public databases have validated the effectiveness of the proposed method.

Keywords

Local binary pattern (LBP) Texture classification Feature extraction Multi-ring local binary pattern (MrLBP) Rotation invariant 

Notes

Acknowledgments

The authors would like to thank MVG and VGG for sharing the LBP code and the VZ_MR8 code, respectively. The authors also thank Zhenhua Guo, Lei Zhang and David Zhang for sharing the source code of LBPVu2GMES. This work was supported by the National Natural Science Foundation of China (No. 60736010) and the Chinese National 863 Grand (No. 2009AA12Z109).

References

  1. 1.
    Tsai DM, Huang TY (2003) Automated surface inspection for statistical textures. Image Vis Comput 21(4):307–323CrossRefGoogle Scholar
  2. 2.
    Shotton J, Winn J, Rother C, Criminisi A (2009) Texton boost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int J Comput Vis 81(1):2–23CrossRefGoogle Scholar
  3. 3.
    Trias-Sanz R, Stamon G, Louchet J (2008) Using colour, texture, and hierarchial segmentation for high-resolution remote sensing. ISPRS J Photogramm Remote Sens 63(2):156–168CrossRefGoogle Scholar
  4. 4.
    Chun YD, Kim NC, Jang IH (2008) Content-based image retrieval using multiresolution color and texture features. IEEE Trans Multimed 10(6):1073–1084CrossRefGoogle Scholar
  5. 5.
    Tesar L, Shimizu A, Smutek D, Kobatake H, Nawano S (2008) Medical image analysis of 3D CT images based on extension of Haralick texture features. Comput Med Imaging Graph 32(6):513–520CrossRefGoogle Scholar
  6. 6.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621Google Scholar
  7. 7.
    Davis L, Johns S, Aggarwal J (1979) Texture analysis using generalized co-occurrence matrices. IEEE Trans Pattern Anal Mach Intell 1(3):251–259CrossRefGoogle Scholar
  8. 8.
    Randen T, Husoy J (1999) Filtering for texture classification: a comparative study. IEEE Trans Pattern Anal Mach Intell 21(4):291–310CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Kashyap RL, Khotanzad A (1986) A model-based method for rotation invariant texture classification. IEEE Trans Pattern Anal Mach Intell 8(4):472–481CrossRefGoogle Scholar
  11. 11.
    Cohen FS, Fan Z, Patel MA (1991) Classification of rotated and scaled textured images using Gaussian Markov random field models. IEEE Trans Pattern Anal Mach Intell 13(2):192–202CrossRefGoogle Scholar
  12. 12.
    Wen-Rong W, Shieh-Chung W (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
  13. 13.
    Jia-Lin C, 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.
    Leung T, Malik J (2001) Representing and recognizing the visual appearance of materials using three-dimensional textons. Int J Comput Vis 43(1):29–44zbMATHCrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Schmid C (2001) Constructing models for content-based image retrieval. In: Proceedings of conference on computer vision and pattern recognition. Montbonnot, France, pp 39–45Google Scholar
  17. 17.
    Varma M, Zisserman A (2005) A statistical approach to texture classification from single images. Int J Comput Vis 62(1):61–81Google Scholar
  18. 18.
    Varma M, Zisserman A (2009) A statistical approach to material classification using image patch exemplars. IEEE Trans Pattern Anal Mach Intell 31(11):2032–2047CrossRefGoogle Scholar
  19. 19.
    Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59CrossRefGoogle Scholar
  20. 20.
    Heikkilä M, Pietikäinen M, Schmid C (2009) Description of interest regions with local binary patterns. Pattern Recogn 42(3):425–436zbMATHCrossRefGoogle Scholar
  21. 21.
    Ahonen T, Hadid A, Pietikäinen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041CrossRefGoogle Scholar
  22. 22.
    Xiaoyang T, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650MathSciNetCrossRefGoogle Scholar
  23. 23.
    Zhang W, Shan S, Qing L, Chen X, Gao W (2009) Are Gabor phases really useless for face recognition? Pattern Anal Appl 12(3):301–307MathSciNetCrossRefGoogle Scholar
  24. 24.
    Nanni L, Lumini A, Brahnam S (2010) Local binary patterns variants as texture descriptors for medical image analysis. Artif Intell Med 49(2):117–125CrossRefGoogle Scholar
  25. 25.
    Mäenpää T, Ojala T, Pietikäinen M, Soriano M (2000) Robust texture classification by subsets of local binary patterns. In: Proceedings of International conference on pattern recognition. Barcelona, Spain, pp 947–950Google Scholar
  26. 26.
    Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  27. 27.
    Zhou H, Wang R, Wang C (2008) A novel extended local-binary-pattern operator for texture analysis. Inf Sci 178(22):4314–4325zbMATHCrossRefGoogle Scholar
  28. 28.
    Guo Z, Zhang L, Zhang D (2010) Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recogn 43(3):706–719zbMATHCrossRefGoogle Scholar
  29. 29.
    Ahonen T, Matas J, He C, Pietikäinen M (2009) Rotation invariant image description with local binary pattern histogram Fourier features. In: Proceedings of 16th Scandinavian conference on image analysis. Oslo, Norway, pp 61–70Google Scholar
  30. 30.
    Shu L, Law M, Chung A (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118CrossRefGoogle Scholar
  31. 31.
    Shu L, Chung A (2009) Face recognition with salient local gradient orientation binary patterns. In: Proceeding of International conference on image processing. Cairo, Egypt, pp 3317–3320Google Scholar
  32. 32.
    Guo Z, Li Q, You J, Zhang D, Liu W (2011) Local directional derivative pattern for rotation invariant texture classification. Neural Comput Appl (in press). doi: 10.1007/s00521-011-0586-6
  33. 33.
    Jie C, Shiguang S, Chu H, Guoying Z, Pietikäinen M, Xilin C, Wen G (2010) WLD: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720CrossRefGoogle Scholar
  34. 34.
    Shi X, Castro ALR, Manduchi R, Montgomery R (2006) Rotational invariant operators based on steerable filter banks. Signal Process Lett 13(11):684–687CrossRefGoogle Scholar
  35. 35.
    Caputo B, Hayman E, Mallikarjuna P (2005) Class-specific material categorisation. In: Proceedings of the 10th IEEE International conference on computer vision. Beijing, China, pp 1597–1604Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.National Key Laboratory of Science and Technology on Multi-spectral Information Processing, Institute for Pattern Recognition and Artificial IntelligenceHuazhong University of Science and TechnologyWuhanPeoples Republic of China

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