A Multiscale Hierarchical Threshold-Based Completed Local Entropy Binary Pattern for Texture Classification

  • Xiaochun Xu
  • Yibing LiEmail author
  • Q. M. Jonathan Wu


Over the year, visual texture analysis has come to be recognized as one of the most important methods in the area of medical image analysis and understanding, face description and detection, and so on. The goal of texture descriptors is to capture the general characteristic of textures such as dependency as well as invariance properties. Among all the texture descriptors, the binary pattern family of algorithms achieves a great trade of representation efficiency and complexity. This work introduces an efficient discriminative texture descriptor for visual texture classification. Its main contribution is twofold: a multiscale thresholding framework based on hierarchical adaptive local partition to binary encoding and an efficient completed local entropy binary pattern (CLEBP) descriptor. The basic completed local entropy binary pattern is extended by multiscale thresholding framework with hierarchical thresholding to capture not only microstructure local patterns but also macrostructure texture information. Such extension improves the quality and discriminative factor of texture classification. Extensive experiments on three widely used benchmark texture databases (Outex, UIUC, and KTH-TIPS) proof the efficiency of the proposed visual texture descriptor and hierarchical thresholding strategy. Compared with some classical local binary pattern variants and many state-of-the-art methods, the proposed descriptor achieves competitive and superior texture classification performance. The results prove that the proposed method is a powerful and effective texture descriptor for visual texture classification.


Feature extraction Multiscale thresholding Completed local entropy binary pattern Texture classification 


Funding Information

The paper is funded by the National Natural Science Foundation of China (Grant No. 61701134, 51,809,056), the National Key Research and Development Program of China (Grant No. 2016YFF0102806), and the Natural Science Foundation of Heilongjiang Province, China (Grant No. F2017004).

Compliance with Ethical Standards

Conflict of Interests

The author declares that she has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Boulkenafet Z, Komulainen J, Hadid A. Face spoofing detection using colour texture analysis. IEEE Trans Inf Forensics Security. 2016;11(8):1818–30.Google Scholar
  2. 2.
    Zhao X, Lin Y, Heikkilä J. Dynamic texture recognition using volume local binary count patterns with an application to 2D face spoofing detection. IEEE Trans Multimedia. 2017;20(3):552–66.Google Scholar
  3. 3.
    Castellano G, Bonilha L, Li L, Cendes F. Texture analysis of medical images. Clin Radiol. 2004;59(12):1061–9.PubMedGoogle Scholar
  4. 4.
    Torabi M, Ardekani RD, Fatemizadeh E. Discrimination between Alzheimer's disease and control group in MR-images based on texture analysis using artificial neural network. Proc IEEE Int Conf Biomed Pharmaceutical Eng. 2006;79–83.Google Scholar
  5. 5.
    Wang YN, Huang JC. Texture analysis in hexagonal materials. Mater Chem Phys. 2003;81:11–26.Google Scholar
  6. 6.
    Akbari V, Doulgeris AP, Moser G, Eltoft T, Anfinsen SN, Serpico SB. A textural–contextual model for unsupervised segmentation of multipolarization synthetic aperture radar images. IEEE Trans Geosci Remote Sens. 2013;51(4):2442–53.Google Scholar
  7. 7.
    Masjedi A, Zoej MJV, Maghsoudi Y. Classification of polarimetric SAR images based on modeling contextual information and using texture features. IEEE Trans Geosci Remote Sens. 2016;54(2):932–43.Google Scholar
  8. 8.
    Liu L, Lao S, Fieguth P, Guo Y, Wang X, Pietikainen M. Median robust extended local binary pattern for texture classification. IEEE Trans Image Process. 2016;25(3):1368–81.PubMedGoogle Scholar
  9. 9.
    Ojala T, Pietikäinen M, Maenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell. 2002;24(7):971–87.Google Scholar
  10. 10.
    Liu L, Fieguth P, Pietikainen M, and Lao S. Median robust extended local binary pattern for texture classification. in Proc IEEE Int Conf Image Process (ICIP). 2015;2319–2323.Google Scholar
  11. 11.
    Subrahmanyam M, Maheshwari R, Balasubramanian R. Local maximum edge binary patterns: a new descriptor for image retrieval and object tracking. Signal Process. 2012;92(6):1467–79.Google Scholar
  12. 12.
    Murala S, Maheshwari RP, Balasubramanian R. Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process. 2012;21(5):2874–86.PubMedGoogle Scholar
  13. 13.
    Ahonen T, Hadid A, Pietikäinen M. Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell. 2006;28(12):2037–41.PubMedGoogle Scholar
  14. 14.
    Ahonen T, Hadid A, Pietikáinen M. Face recognition with local binary patterns. Proc Eur Conf Comput Vis. 2004; 469–481.Google Scholar
  15. 15.
    Tan X, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process. 2010;19(6):1635–50.PubMedGoogle Scholar
  16. 16.
    Nanni L, Lumini A, Brahnam S. Local binary patterns variants as texture descriptors for medical image analysis. Artif Intell Med. 2010;49(2):117–25.PubMedGoogle Scholar
  17. 17.
    Satpathy A, Jiang X, Eng H. LBP based edge texture features for object recognition. IEEE Trans Image Process. 2014;23(5):1953–64.PubMedGoogle Scholar
  18. 18.
    Guo Z, Zhang L, Zhang D. A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process. 2010;9(16):1657–63.Google Scholar
  19. 19.
    Guo Z, Zhang L, Zhang D. Rotation invariant texture classification using lbp variance (LBPV) with global matching. Pattern Recogn. 2010;43(3):706–19.Google Scholar
  20. 20.
    Zhao Y, Huang DS, Jia W. Completed local binary count for rotation invariant texture classification. IEEE Trans Image Process. 2012;20(10):4492–7.Google Scholar
  21. 21.
    Zhao Y, Jia W, Hu RX, Min H. Completed robust local binary pattern for texture classification. Neurocomputing. 2013;106(1):68–76.Google Scholar
  22. 22.
    Song K, Yan Y, Zhao Y, Liu C. Adjacent evaluation of local binary pattern for texture classification. J Vis Commun Image Represent. 2015;33:323–39.Google Scholar
  23. 23.
    Guo Z, Wang X, Zhou J, You J. Robust texture image representation by scale selective local binary patterns. IEEE Trans Image Process. 2016;25(2):687–99.PubMedGoogle Scholar
  24. 24.
    Mehta R, Egiazarian KO. Dominant rotated local binary patterns (DRLBP) for texture classification. Pattern Recogn Lett. 2016;71:16–22.Google Scholar
  25. 25.
    Pan ZB, Li ZY, Fan HC, Wu XQ. Feature based local binary pattern for rotation invariant texture classification. Expert Syst Appl. 2017;88:238–48.Google Scholar
  26. 26.
    Wang K, Bichot CE, Li Y, Li BL. Local binary circumferential and radial derivative pattern for texture classification. Pattern Recogn. 2017;67:213–29.Google Scholar
  27. 27.
    Shakoor M, Boostani R. Extended mapping local binary pattern operator for texture classification. Int J Pattern Recognit. 2017;31(6):1–22.Google Scholar
  28. 28.
    Zhang Z, Liu S, Mei X, Xiao B, Zhang L. Learning completed discriminative local features for texture classification. Pattern Recogn. 2017;67:263–75.Google Scholar
  29. 29.
    Yu J, Kuang Z, Zhang B, Zhang W, Lin D, Fan J. Leveraging content sensitiveness and user trustworthiness to recommend fine-grained privacy settings for social image sharing[J]. IEEE Trans Inf Forensics Secur. 2018;13(5):1317–32.Google Scholar
  30. 30.
    Zhang J, Yu J, Tao D. Local deep-feature alignment for unsupervised dimension reduction[J]. IEEE Trans Image Process. 2018;24(5):2420–32.Google Scholar
  31. 31.
    Yu J, Rui Y, Tao D. Click prediction for web image Reranking using multimodal sparse coding[J]. IEEE Trans Image Process. 2014;23(5):2019–32.PubMedGoogle Scholar
  32. 32.
    Yu J, Yang X, Gao F, et al. Deep multimodal distance metric learning using click constraints for image ranking[J]. IEEE Trans Cybern. 2016;1–11.Google Scholar
  33. 33.
    Thangarajah A, Jonathan WQ, Hui Z. Effect of fusing features from multiple DCNN architectures in image classification[J]. IET Image Process. 2018;12(7):1102–10.Google Scholar
  34. 34.
    Akilan T, Wu QMJ, Safaei A, Jiang W. A late fusion approach for harnessing multi-CNN model high-level features. 2017 IEEE International Confere nce on Systems. 2017; 1–8.Google Scholar
  35. 35.
    Lin TY and Maji S. Visualizing and understanding deep texture representations. in Proc IEEE Conf Comput Vis Pattern Recogn. 2016; 2791–2799.Google Scholar
  36. 36.
    Yu J, Tao D, Wang M, Rui Y. Learning to rank using user clicks and visual features for image retrieval[J]. IEEE Transactions on Cybernetics. 2015;45(4):767–79.PubMedGoogle Scholar
  37. 37.
    Yu J, Rui Y, Tang YY, Tao D. High-order distance-based multiview stochastic learning in image classification[J]. IEEE Transactions on Cybernetics. 2014;44(12):2431–42.PubMedGoogle Scholar
  38. 38.
    Zhong G, Yan S, Huang K, Cai Y, Dong J. Reducing and stretching deep convolutional activation features for accurate image classification[J]. Cogn Comput. 2018;10(1):179–86.Google Scholar
  39. 39.
    Srikanth V, Neerja M, Chakravarthy NB, et al. 3D local spatio-temporal ternary patterns for moving object detection in complex scenes[J]. Cogn Comput. 2019;11(1):18–30.Google Scholar
  40. 40.
    Toprak S, Nicolás N-G, Wermter S. Evaluating integration strategies for visuo-haptic object recognition[J]. Cogn Comput. 2018;10(3):408–25.Google Scholar
  41. 41.
    Yu J, Zhu C, Zhang J, et al. Spatial pyramid-enhanced NetVLAD with weighted triplet loss for place recognition[J]. IEEE Trans Neural Netw Learn Syst. 2019;1–14.Google Scholar
  42. 42.
    Zrira N, Khan HA, Bouyakhf EH. Discriminative deep belief network for indoor environment classification using global visual features[J]. Cogn Comput. 2018;10(3):437–53.Google Scholar
  43. 43.
    Hong C, Yu J, Chen X, et al. Image-based 3D human pose recovery with locality sensitive sparse retrieval. in Proc. IEEE International Conference on Systems. 2013; 1–6.Google Scholar
  44. 44.
    Hong C, Yu J, Wan J, Tao D, Wang M. Multimodal deep autoencoder for human pose recovery[J]. IEEE Trans Image Process. 2015;24(12):5659–70.PubMedGoogle Scholar
  45. 45.
    Yang ZX, Tang L, Zhang K, Wong PK. Multi-view CNN feature aggregation with ELM auto-encoder for 3D shape recognition[J]. Cogn Comput. 2018;10(6):908–21.Google Scholar
  46. 46.
    Li J, Zhang Z, He H. Hierarchical convolutional neural networks for EEG-based emotion recognition[J]. Cogn Comput. 2017;10(2):368–80.Google Scholar
  47. 47.
    Ren CX, Lei Z, Dai DQ, Li SZ. Enhanced local gradient order features and discriminant analysis for face recognition. IEEE Trans Image Process. 2016;22(5):2656–69.Google Scholar
  48. 48.
    Huang W, Yin H. Robust face recognition with structural binary gradient patterns. Pattern Recogn. 2017;68:126–40.Google Scholar
  49. 49.
    Ojala T, Maenpaa T, Pietikainen M, Viertola J, Kyllonen J, and Huovinen S. Outex – new framework for empirical evaluation of texture analysis algorithms. In Proc IEEE Int Conf Pattern Recognit (ICPR). 2002; 701–706.Google Scholar
  50. 50.
    Dana KJ, Van Ginneken B, Nayar SK, Koenderink JJ. Reflection and texture of real-word surfaces. ACM Trans Graph. 1999;18(1):1–34.Google Scholar
  51. 51.
    Hayman E, Caputo B, Fritz M, and Eklundh J. On the significance of real-world conditions for material classification. In Proc Eur Conf Comput Vis. 2004; 253–266.Google Scholar
  52. 52.
    Liu L, Long Y, Fieguth PW, Lao S, Zhao G. BRINT: binary rotation invariant and noise tolerant texture classification. IEEE Trans Image Process. 2014;23(7):3071–84.PubMedGoogle Scholar
  53. 53.
    Zhao Y, Wang R G , Wang W M , et al. Local quantization code histogram for texture classification. Neurocomputing. 2016; 354–364.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information and Communication EngineeringHarbin Engineering UniversityHarbinChina
  2. 2.Department of Electrical and Computer Engineering University of WindsorWindsorCanada

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