Multimedia Tools and Applications

, Volume 77, Issue 20, pp 26469–26484 | Cite as

A new encoding scheme of LBP based on maximum run length of state “1” for texture classification

  • Zhibin PanEmail author
  • Zhengyi Li
  • Xiuquan Wu


As a simple and efficient local feature descriptor, local binary pattern (LBP) is mainly made up of two steps: extraction step and encoding step. In the extraction step, a local region is denoted by a difference vector between the center pixel and its neighbors. In the encoding step, the corresponding binary bit-string of the difference vector is encoded for the following texture classification. Though encoding step plays a vital role in the whole process of LBP, two current widely used encoding schemes of LBPriu2 and LBC still have some limitations. Different from these two current encoding schemes, in this paper, we propose a new LBP encoding scheme based on the maximum run length of state “1” (LBPmr1) in a binary bit-string. The maximum run length of state “1” reflects the most important part of the binary bit-string structure and it is used as the LBP code of a binary bit-string for the first time. Experimental results on four representative texture databases of Outex, UIUC, CUReT and UMD show that the proposed LBPmr1 achieves better classification accuracy compared with other related LBP encoding schemes.


Local binary pattern (LBP) Local binary count (LBC) Encoding scheme Maximum run length State “1” 



This work is supported in part by the Industrial Program of Zhejiang Province (Grant No. Grant No. 2016C31090), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences (Grant No. LSIT201606D) and the Key Science and Technology Program of Shaanxi Province (Grant No. 2016GY-097).


  1. 1.
    Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359CrossRefGoogle Scholar
  2. 2.
    Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst 43(4):996–1002CrossRefGoogle Scholar
  3. 3.
    Dana KJ, Ginneken BV, Nayar SK, Koenderink JJ (1997) Reflectance and texture and of real. 2013. IEEE Conf Comput Vis Pattern Recognit 18:151CrossRefGoogle Scholar
  4. 4.
    Jiang J, Chen C, Ma J, Wang Z, Wang Z, Hu R (2017) SRLSP: A face image super-resolution algorithm using smooth regression with local structure prior. IEEE Trans Multimed 19(1):27–40CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Li J, Li X, Yang B, Sun X (2017) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518Google Scholar
  7. 7.
    Liao S, Law MWK, Chung ACS (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118MathSciNetCrossRefGoogle Scholar
  8. 8.
    Liu L et al (2014) BRINT: binary rotation invariant and noise tolerant texture classification. IEEE Trans Image Process 23(7):3071–3084MathSciNetCrossRefGoogle Scholar
  9. 9.
    Liu L, Wiliem A, Chen S, Lovell BC (2014) Automatic image attribute selection for zero-shot learning of object categories. In: International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, pp 2619–2624Google Scholar
  10. 10.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data. In: Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, pp 1617–1623Google Scholar
  11. 11.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016a) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  12. 12.
    Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016b) Recognizing complex activities by a probabilistic interval-based model. In: AAAI Conference on Artificial Intelligence, North America, pp 1266–1272Google Scholar
  13. 13.
    Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016c) Urban water quality prediction based on multi-task multi-view learning. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), New York, UK, pp 19–25Google Scholar
  14. 14.
    Liu L, Wiliem A, Chen S, Lovell BC (2016d) What is the best way for extracting meaningful attributes from pictures? Pattern Recogn 64(C):314–326Google Scholar
  15. 15.
    Liu L, Nie F, Zhang T, Wiliem A, Lovell BC (2017) Unsupervised automatic attribute discovery method via multi-graph clustering. In: International Conference on Pattern Recognition (ICPR), Cancún, México, pp 1713–1718Google Scholar
  16. 16.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  17. 17.
    Lu Y, Wei Y, Liu L, Zhong J, Sun L, Liu Y (2017) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimed Tools Appl 76(8):10701–10719CrossRefGoogle Scholar
  18. 18.
    Lu T, Xiong Z, Zhang Y, Wang B, Lu T (2017) Robust face super-resolution via locality-constrained low-rank representation. IEEE Access 5:13103–13117CrossRefGoogle Scholar
  19. 19.
    Ludwig O, Delgado D, Goncalves V, Nunes U (2009) Trainable classifier-fusion schemes: an application to pedestrian detection. In: International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA, pp 1–6Google Scholar
  20. 20.
    Ma J, Zhao J, Guo H, Jiang J, Zhou H, Gao Y (2017) Locality preserving matching. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp 4492–4498.
  21. 21.
    Ojala T, Pietikainen M, Maenpaa 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
  22. 22.
    Ojala T, Maenpaa T, Pietikainen M, Viertola J, Kyllönen J, Huovinen S (2002) Outex - new framework for empirical evaluation of texture analysis algorithms. In: Proceedings. 16th International Conference on Pattern Recognition, 2002, Quebec, Canada, vol 1, pp 701–706Google Scholar
  23. 23.
    Pan Z, Fan H, Zhang L (2015) Texture Classification Using Local Pattern Based on Vector Quantization. IEEE Trans Image Process 24:5379–5388MathSciNetCrossRefGoogle Scholar
  24. 24.
    Pan Z, Wu X, Li Z, Zhou Z (2017) Local adaptive binary patterns using diamond sampling structure for texture classification. IEEE Sign Process Lett 24(6):828–832CrossRefGoogle Scholar
  25. 25.
    Pan Z, Li Z, Fan H, Wu X (2017) Feature based local binary pattern for rotation invariant texture classification. Expert Syst Appl 88:238–248CrossRefGoogle Scholar
  26. 26.
    Rani PI, Muneeswaran K (2017) Recognize the facial emotion in video sequences using eye and mouth temporal Gabor features. Multimed Tools Appl 76(7):1–24CrossRefGoogle Scholar
  27. 27.
    Ren J, Jiang X, Yuan J (2013) Noise-resistant local binary pattern with an embedded error-correction mechanism. IEEE Trans Image Process 22(10):4049–4060MathSciNetCrossRefGoogle Scholar
  28. 28.
    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
  29. 29.
    Wang K et al (2013) Pixel to Patch Sampling Structure and Local Neighboring Intensity Relationship Patterns for Texture Classification. IEEE Signal Process Lett 20(9):853–856CrossRefGoogle Scholar
  30. 30.
    Yang Z, De-Shuang H, Wei J (2012) Completed Local Binary Count for Rotation Invariant Texture Classification. IEEE Trans Image Process 21:4492–4497MathSciNetCrossRefGoogle Scholar
  31. 31.
    Xu Y, Yang X, Ling H, Ji H (2010) A new texture descriptor using multifractal analysis in multi-orientation wavelet pyramid. In: Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA, pp 161–168Google Scholar
  32. 32.
    Yuan C, Sun X, Lv R (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun 13(7):60–65CrossRefGoogle Scholar
  33. 33.
    Zhang T, Liu L, Wiliem A, Lovell B (2016). Is Alice chasing or being chased?: determining subject and object of activities in videos. In: IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, pp 1–7Google Scholar
  34. 34.
    Zhenhua G, Zhang D, Zhang D (2010) A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE Trans Image Process 19:1657–1663MathSciNetCrossRefGoogle Scholar
  35. 35.
    Zhou Z, Wang Y, Wu QMJ, Yang CN, Sun X (2016) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Secur 12(1):48–63CrossRefGoogle Scholar
  36. 36.
    Zhou Z, Yang CN, Chen B, Sun X, Liu Q, Wu QMJ (2016) Effective and efficient image copy detection with resistance to arbitrary rotation. IEICE Trans Inf Syst E99.D(6):1531–1540CrossRefGoogle Scholar
  37. 37.
    Zhou Z, Wu QMJ, Huang F, Sun X (2017) Fast and accurate near-duplicate image elimination for visual sensor networks. Int J Distrib Sens Netw 13(2). CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anPeople’s Republic of China

Personalised recommendations