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Exploring joint encoding of multi-direction local binary patterns for image classification

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Abstract

Local binary pattern (LBP) has been investigated as prominent feature in image classification for decades because of its strong discriminative ability and simple computation complexity, however, it extracts patterns in local circular space, ignoring the patterns located in local line-geometry space. In this paper, we propose a novel feature named joint encoding of multi-direction LBP (JEMDLBP) for image representation. Concretely, due to direction information are highly valuable to reflect the fundamental properties of images, we firstly develop direction LBP (DLBP) codes in local line space of four directions, including 0∘, 45∘, 90∘ and 135∘ directions. Secondly, the DLBP dominant patterns are generated by employing statistical analysis based on the occurrence rates of DLBP codes, which has high adaptive power to database and direction. Thirdly, we employ joint encoding strategy to capture co-occurrence patterns between adjacent directions with the hope that stronger local line structure can be extracted. Finally, to validate the effectiveness and efficiency of JEMDLBP, extensive experiments are carried out on eight databases (four face recognition, one face expression recognition, one palmprint recognition, two texture classification). Excellent results show that JEMDLBP achieves a performance that is competitive or better than several related LBP-like descriptors.

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Notes

  1. code: https://www.dropbox.com/s/6bozay2wx8j3iml/MSJLBP.zip

  2. code: http://parnec.nuaa.edu.cn/xtan/

  3. code: http://www4.comp.polyu.edu.hk/cslzhang/code/CLBP.rar

References

  1. 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):2037–2041

    Article  MATH  Google Scholar 

  2. Ahonen T, Matas J, He C, Pietikäinen M (2009) Rotation invariant image description with local binary pattern histogram fourier features. In: Image analysis, pp 61–70

  3. Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (SURF). Comput Vision Image Underst 110:346–359

    Article  Google Scholar 

  4. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  5. Brodatz P (1996) Textures: a photographic album for artists and designers. Dover Publications, New York

    Google Scholar 

  6. Calonder M, Lepetit V, Strecha C, Fua P (2010) BRIEF: binary robust independent elementary features. In: Proceedings of the european conference on computer vision (ECCV), pp 778–792

  7. Chakraborty S, Singh SK, Chakraborty P (2017) Local directional gradient pattern a local descriptor for face recognition. Multimedia Tools and Applications 76(1):1201–1216

    Article  Google Scholar 

  8. Chan C-H, Kittler J, Messer K (2007) Multi-scale local binary pattern histograms for face recognition. In: Proceedings of international conference on biometrics (ICB), pp 809–818

  9. Csurka G, Dance CR, Fan LX, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in european conference on computer vision (ECCV), pp 1–22

  10. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of IEEE international conference on computer vision and pattern recognition (CVPR), pp 20–25

  11. Guo Z, Wang X, Zhou J, You J (2016) Robust texture image representation by scale selective local binary patterns. IEEE Trans Image Process 25(2):687–699

    Article  MathSciNet  Google Scholar 

  12. Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663

    Article  MathSciNet  MATH  Google Scholar 

  13. Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  Google Scholar 

  14. He ZY, You XG, Yuan Y (2009) Texture image retrieval based on non-tensor product wavelet filter banks. Signal Process 89(8):1501–1510

    Article  MATH  Google Scholar 

  15. Hegenbart S, Uhl K (2015) A scale-and orientation-adaptive extension of local binary patterns for texture classification. Pattern Recog 48:2633–2644

    Article  Google Scholar 

  16. Heikkilä M, Pietikäinen M, Schmid C (2009) Description of interest regions with local binary patterns. Pattern Recog 42:425–436

    Article  MATH  Google Scholar 

  17. Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Tech. rep., Technical Report 07-49. University of Massachusetts, Amherst

    Google Scholar 

  18. Jabid T, Kabir MH, Chae O (2010) Facial expression recognition using local directional pattern (LDP). In: Proceedings of IEEE international conference on image processing (ICIP), pp 1605–1608

  19. Kaya Y, Ertugrul ÖF, Tekin R (2015) Two novel local binary pattern descriptors for texture analysis. Appl Soft Comput 34:728–735

    Article  Google Scholar 

  20. Kylberg G The Kylberg Texture Dataset v. 1.0, Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, External report (Blue series) No. 35. Available online at: http://www.cb.uu.se/~gustaf/texture/

  21. Lei Z, Liao S, Pietikäinen M, Li SZ (2011) Face recognition by exploring information jointly in space, scale and direction. IEEE Trans Image Process 20(1):247–256

    Article  MathSciNet  Google Scholar 

  22. Li J, Sang N, Gao CX (2016) LEDTD: Local edge direction and texture descriptor for face recognition. Signal Processing Image Communication 41:40–45

    Article  Google Scholar 

  23. Li W, Fritz M (2012) Recognizing materials from virtual examples. In: Proceedings of european conference on computer vision (ECCV), pp 345–358

  24. Liao S, Law MWK, Chung ACS (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118

    Article  MathSciNet  MATH  Google Scholar 

  25. Liao S, Zhao G, Kellokumpu V, Pietikäinen M, Li SZ (2010) Modeling Pixel Process with Scale Invariant Local Patterns for Background Subtraction in Complex Scenes. In: Proceedings of IEEE international conference on computer vision and pattern recognition (CVPR), pp 1301– 1306

  26. Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans Image Process 11 (4):467–476

    Article  Google Scholar 

  27. Liu L, Fieguth P, Zhao G, Pietikäinen M, Hu D (2016) Extended local binary patterns for face recognition. Inf Sci 358(1):358–359

    Google Scholar 

  28. Liu L, Lao S, Fieguth P, Guo Y, Wang X, Pietikäinen M (2016) Median robust extended local binary pattern for texture classification. IEEE Trans Image Process 25(3):1368–1381

    Article  MathSciNet  Google Scholar 

  29. Lowe DG (2004) Distinctive image features from Scale-Invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  30. Luo Y-T, Zhao L-Y, Zhang B, Jia W, Xue F, Lu J-T, Zhu Y-H, Xu B-Q (2016) Local line directional pattern for palmprint recognition. Pattern Recog 50:26–44

    Article  Google Scholar 

  31. Lyons MJ, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with gabor wavelets. In: Proceedings of international conference on automatic face and gesture recognition, pp 200–205

  32. Martínez A, Benavente R (1998) The AR face database

  33. Nanni L, Brahnam S, Lumini A (2010) A local approach based on a local binary patterns variant texture descriptor for classifying pain states. Expert Syst Appl 37(12):7888–7894

    Article  Google Scholar 

  34. Nosaka R, Ohkawa Y, Fukui K (2011) Feature extraction based on co-occurrence of adjacent local binary patterns. Advances in Image and Video Technology pp 82–91

  35. Nosaka R, Suryanto CH, Fukui K (2012) Rotation invariant co-occurrence among adjacent LBPs. In: Proceedings of asian conference on computer vision (ACCV) workshop, pp 15–25

  36. 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):971–987

    Article  MATH  Google Scholar 

  37. Pan B, Lai J, Chen WS (2011) Nonlinear nonnegative matrix factorization based on Mercer kernel construction. Pattern Recog 44(10):2800–2810

    Article  MATH  Google Scholar 

  38. Pan ZB, Fan HC, Zhang L (2015) Texture classification using local pattern based on vector quantization. IEEE Trans Image Process 24(12):5379–5388

    Article  MathSciNet  Google Scholar 

  39. Petpon A, Srisuk S (2009) Face Recognition with Local Line Binary Pattern. In: Proceedings of international conference on image graph (ICIG), pp 533–539

  40. Phillips PJ, Wechsler H, Huang JS, Rauss PJ (1998) The FERET database and evaluation procedure for face recognition algorithms. Image Vis Comput 16(5):295–306

    Article  Google Scholar 

  41. Pietikäinen M, Hadid A, Zhao G, Ahonen T (2011) Computer vision using local binary patterns. Springer, London

    Book  Google Scholar 

  42. Qi X, Qiao Y, Li C, Guo J (2013) Multi-scale joint encoding of local binary patterns for texture and material classification. In: Proceedings of british machine vision conference (BMVC), pp 1–11

  43. Qi X, Shen L, Zhao G, Li Q, Pietikäinen M (2015) Globally rotation invariant multi-scale co-occurrence local binary pattern. Image Vis Comput 43:16–26

    Article  Google Scholar 

  44. Qi X, Xiao R, Li C, Qiao Y, Guo J, Tang X (2014) Pairwise rotation invariant Co-Occurrence local binary pattern. IEEE Trans Pattern Anal Mach Intell 36(11):2199–2213

    Article  Google Scholar 

  45. Ramalingam SP, Paturu Venkata Subbu Sita Rama CM (2016) J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-016-0408-x

  46. Rivera AR, Castillo JR, Chae O (2013) Local directional number pattern for face analysis face and expression recognition. IEEE Trans Image Process 20(5):1740–1752

    Article  MathSciNet  MATH  Google Scholar 

  47. Shan C, Gong S, McOwen P (2009) Facial expression recognition based on local binary patterns: A comprehensive study. Image Vis Comput 27(6):803–816

    Article  Google Scholar 

  48. Srinivasa Perumal R, Chandra Mouli P (2016) Dimensionality reduced local directional pattern (DR-LDP) for face recognition. Expert Syst Appl 63:66–73

    Article  Google Scholar 

  49. Srinivasa Perumal R, Chandra Mouli P (2016) Two-level dimensionality reduced local directional pattern for face recognition. Int J Biometrics 8(1):52–64

    Article  Google Scholar 

  50. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650

    Article  MathSciNet  MATH  Google Scholar 

  51. Tola E, Lepetit V, Fua P (2010) DAISY: An efficient dense descriptor applied to widebaseline stereo. IEEE Trans Pattern Anal Mach Intell 32(5):815–830

    Article  Google Scholar 

  52. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3 (1):71C86

    Article  Google Scholar 

  53. Verma M, Raman B (2015) Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval. J Vis Commun Image R 32:224–236

    Article  Google Scholar 

  54. Werghi N, Berretti S, Bimbo AD (2015) The mesh-LBP: A framework for extracting local binary patterns from discrete manifolds. IEEE Trans Image Process 24 (1):220–235

    Article  MathSciNet  Google Scholar 

  55. Wolf L, Hassner T, Taigman Y (2009) Similarity scores based on background samples. In: Proceedings of asian conference on computer vision (ACCV), pp 88–97

  56. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31 (2):210–227

    Article  Google Scholar 

  57. Xu Y, Li XL, Yang J, Lai ZH, Zhang D (2014) Integrating conventional and inverse representation for face recognition. IEEE Transactions on Cybernetics 44(10):1738–1746

    Article  Google Scholar 

  58. Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  59. Zhang BC, Gao YS, Zhao SQ, Liu JZ (2010) Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor. IEEE Trans Image Process 19(2):533–544

    Article  MathSciNet  MATH  Google Scholar 

  60. Zhang D, Kong W-K, You J, Wong M (2003) On-line palmprint identification. IEEE Trans Pattern Anal Mach Intell 25(9):1041–1050

    Article  Google Scholar 

  61. Zhang D, Guo ZH, Lu GM, Zhang L, Zuo WM (2010) An online system of multi-spectral palmprint verification. IEEE Trans Instrum Meas 59(2):480–490

    Article  Google Scholar 

  62. Zhao G, Ahonen T, Matas J, Pietikäinen M (2012) Rotation-Invariant Image and Video Description With Local Binary Pattern Features. IEEE Trans Image Process 21(4):1465–1477

    Article  MathSciNet  MATH  Google Scholar 

  63. Zhao Y, Huang D, Jia W (2012) Completed local binary count for rotation invariant texture classification. IEEE Trans Image Process 21(10):4492–4497

    Article  MathSciNet  MATH  Google Scholar 

  64. Zhong FJ, Zhang JS (2013) Face recognition with enhanced local directional patterns. Neurocomputing 119:375–384

    Article  Google Scholar 

  65. Zhu C, Wang R (2012) Local multiple patterns based multiresolution gray-scale and rotation invariant texture classification. Inf Sci 187:93–108

    Article  Google Scholar 

  66. Zhu P, Zhang L, Hu QH, Shiu SCK (2012) Multi-scale patch based collaborative representation for face recognition with margin distribution optimization. In: Proceedings of the european conference on computer vision (ECCV), pp 822–835

  67. Zhu ZQ, You XG, Chen C, Tao DC, Ou WH, Jiang XB, Zou JX (2015) An adaptive hybrid pattern for noise-robust texture analysis. Pattern Recog 48(8):2592–2608

    Article  Google Scholar 

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Acknowledgments

The work described in this paper was partially supported by the National Natural Science Foundation of China (Grant no. 61772093, 61402062, 61602068), Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT1196).

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Authors and Affiliations

  1. School of Software Engineering, Chongqing University, Chongqing, 401331, China

    Daoxiang Zhou, Dan Yang & Xiaohong Zhang

  2. Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education, Chongqing, 400044, China

    Xiaohong Zhang

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  1. Daoxiang Zhou
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  2. Dan Yang
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  3. Xiaohong Zhang
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Correspondence to Daoxiang Zhou.

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Zhou, D., Yang, D. & Zhang, X. Exploring joint encoding of multi-direction local binary patterns for image classification. Multimed Tools Appl 77, 18957–18981 (2018). https://doi.org/10.1007/s11042-017-5319-0

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