Skip to main content
Log in

Multi-layer local energy patterns for texture representation and classification

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Motivated by the recent success of deep networks in providing effective and abstract image representations, in this paper, a multi-layer architecture called the multi-layer local energy patterns (ML-LEP) is proposed for texture representation and classification. The proposed approach follows a multi-layer convolutional neural network paradigm and is built upon the single-layer local energy pattern (LEP) approach, a statistical histogram-based method for texture representation. An important aspect of the proposed multi-layer method compared to other deep convolutional architectures is bypassing the computationally expensive learning stage using fixed filters. As such, the proposed training-free network circumvents the need for large data for learning system parameters. An extensive investigation is carried out to determine the merits of different nonlinear operators in the proposed architecture. For this purpose, different nonlinearities including an energy-based nonlinearity, the absolute operator as well as the rectifier functions are extensively investigated and compared against each other. Extensive experiments conducted on three challenging databases of KTH-TIPS, KTH-TIPS2-a and the UIUC indicate that the extension of the LEP method to the multi-layer LEP is effective and leads to better performance. Moreover, the proposed ML-LEP approach is compared to several other well-known descriptors in the field, achieving the best reported performance on the KTH-TIPS and the KTH-TIPS2-a databases despite being training-free.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Ji, Q., Engel, J., Craine, E.: Texture analysis for classification of cervix lesions. IEEE Trans. Med. Imaging 19(11), 1144–1149 (2000)

    Article  Google Scholar 

  2. Wang, X.F., Huang, D.S., Du, J.X., Xu, H., Heutte, L.: Classification of plant leaf images with complicated background. Appl. Math. Comput. 205(2), 916–926 (2008)

    MathSciNet  MATH  Google Scholar 

  3. Tsai, D.M., Huang, T.Y.: Automated surface inspection for statistical textures. Image Vis. Comput. 21(4), 307–323 (2003)

    Article  Google Scholar 

  4. Santos, J.A.D., Gosselin, P.H., Philipp-Foliguet, S., Torres, R.D.S., Falao, A.X.: Multiscale classification of remote sensing images. IEEE Trans. Geosci. Remote Sens. 50(10), 3764–3775 (2012)

    Article  Google Scholar 

  5. Huang, C.R., Chen, C.S., Chung, P.C.: Contrast context histogram: an efficient discriminating local descriptor for object recognition and image matching. Pattern Recogn. 41(10), 3071–3077 (2008)

    Article  MATH  Google Scholar 

  6. Zhu, Y., Tan, T., Wang, Y.: Font recognition based on global texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1192–1200 (2001)

    Article  Google Scholar 

  7. Hanbay, K., Talu, M.F.: Segmentation of SAR images using improved artificial bee colony algorithm and neutrosophic set. Appl. Soft Comput. 21, 433–443 (2014)

    Article  Google Scholar 

  8. Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  12. Guo, Z., Zhang, L., Zhang, D.: Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recogn. 43(3), 706–719 (2010)

    Article  MATH  Google Scholar 

  13. Guo, Z., Zhang, L., Zhang, D., Zhang, S.: Rotation invariant texture classification using adaptive LBP with directional statistical features. In: International Conference on Image Processing (ICIP), pp. 285–288 (2010)

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

    Article  MathSciNet  Google Scholar 

  15. Hong, X., Zhao, G., Pietikainen, M., Chen, X.: Combining lbp difference and feature correlation for texture description. IEEE Trans. Image Process. 23(6), 2557–2568 (2014)

    Article  MathSciNet  Google Scholar 

  16. Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vis. 62(1–2), 61–81 (2005)

    Article  Google Scholar 

  17. Varma, M., Garg, R.: Locally invariant fractal features for statistical texture classification. In: International Conference on Computer Vision, pp. 1–8 (2007)

  18. Varma, M., Zisserman, A.: A statistical approach to material classification using image patch exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2032–2047 (2009)

    Article  Google Scholar 

  19. Zhang, J., Zhao, H., Liang, J.: Continuous rotation invariant local descriptors for texton dictionary-based texture classification. Comput. Vis. Image Underst. 117(1), 56–75 (2013)

    Article  MathSciNet  Google Scholar 

  20. Crosier, M., Griffin, L.D.: Using basic image features for texture classification. Int. J. Comput. Vis. 88(3), 447–460 (2010)

    Article  MathSciNet  Google Scholar 

  21. Chen, J., Shan, S., He, C., Zhao, G., Pietikäinen, M., Chen, X., Gao, W.: WLD: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1705–1720 (2010)

    Article  Google Scholar 

  22. Zhang, J., Liang, J., Zhao, H.: Local energy pattern for texture classification using self-adaptive quantization thresholds. IEEE Trans. Image Process. 22(1), 31–42 (2013)

    Article  MathSciNet  Google Scholar 

  23. Shrivastava, N., Tyagi, V.: An effective scheme for image texture classification based on binary local structure pattern. Visual Comput. 30(11), 1223–1232 (2014)

    Article  Google Scholar 

  24. Wu, X., Sun, J.: Joint-scale LBP: a new feature descriptor for texture classification. Visual Comput., 1–13 (2015). doi:10.1007/s00371-015-1202-z

  25. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  26. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649 (2012)

  27. Zhou, S., Chen, Q., Wang, X.: Active deep learning method for semi-supervised sentiment classification. Neurocomputing 120, 536–546 (2013)

    Article  Google Scholar 

  28. Zhong, S.-h., Liu, Y., Liu, Y.: Bilinear deep learning for image classification. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 343–352 (2011)

  29. Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: PCANet: a simple deep learning baseline for image classification? (2014). arXiv:1404.3606

  30. Socher, R., Huval, B., Bath, B., Manning, C.D., Ng, A.Y.: Convolutional-recursive deep learning for 3d object classification. In: Advances in Neural Information Processing Systems, pp. 665–673 (2012)

  31. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  32. Bengio, Y., Goodfellow, I.J., Courville, A.: Deep Learning. http://www.iro.umontreal.ca/~bengioy/dlbook (2015)

  33. Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 9, 891–906 (1991)

    Article  Google Scholar 

  34. Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the significance of real-world conditions for material classification. In: Computer Vision-ECCV 2004. pp. 253–266. Springer, New York (2004)

  35. Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: International Conference on Computer Vision, pp. 1597–1604 (2005)

  36. Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1265–1278 (2005)

    Article  Google Scholar 

  37. Zhang, J., Marszałek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis. 73(2), 213–238 (2007)

    Article  Google Scholar 

  38. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  39. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition. CVPR 2005. IEEE Computer Society Conference on 2005, pp. 886–893. IEEE (2005)

  40. Quan, Y., Xu, Y., Sun, Y., Luo, Y.: Lacunarity analysis on image patterns for texture classification. In: International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 160–167 (2014)

  41. Quan, Y., Xu, Y., Sun, Y.: A distinct and compact texture descriptor. Image Vis. Comput. 32(4), 250–259 (2014)

    Article  Google Scholar 

  42. Xu, Y., Ji, H., Fermüller, C.: Viewpoint invariant texture description using fractal analysis. Int. J. Comput. Vis. 83(1), 85–100 (2009)

    Article  Google Scholar 

  43. Ji, H., Yang, X., Ling, H., Xu, Y.: Wavelet domain multifractal analysis for static and dynamic texture classification. IEEE Trans. Image Process. 22(1), 286–299 (2013)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shervin Rahimzadeh Arashloo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amirolad, A., Arashloo, S.R. & Amirani, M.C. Multi-layer local energy patterns for texture representation and classification. Vis Comput 32, 1633–1644 (2016). https://doi.org/10.1007/s00371-016-1220-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-016-1220-5

Keywords

Navigation