TexFusionNet: An Ensemble of Deep CNN Feature for Texture Classification

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1024)


The texture classification from images is one of the important problems in pattern recognition. Several hand-designed methods have been proposed in last few decades for this problem. Nowadays, it is observed that the convolutional neural networks (CNN) perform extremely well for the classification task mainly over object and scene images. This improved performance of CNN is caused by the availability of huge amount of images in object and scene databases such as ImageNet. Still, the focus of CNN in texture classification is limited due to non-availability of large texture image data sets. Thus, the trained CNN over Imagenet database is used for texture classification by fine-tuning the last few layers of the network. In this paper, a fused CNN (TexFusionNet) is proposed for texture classification by fusing the last representation layer of widely adapted AlexNet and VGG16. On the top of the fused layer, a fully connected layer is used to generate the class score. The categorical cross-entropy loss is used to generate the error during training, which is used to train the added layer after the fusion layer. The results are computed over several well-known Brodatz, CUReT, and KTH-TIPS texture data sets and compared with the state-of-the-art texture classification methods. The experimental results confirm outstanding performance of the proposed TexFusionNet architecture for texture classification.


Convolutional neural network (CNN) Deep learning Texture classification Fusion 


  1. 1.
    Tuceryan, M., Jain, A.K., et al.: Texture analysis. In: Handbook of Pattern Recognition and Computer Vision, vol. 2, pp. 207–248 (1993)CrossRefGoogle Scholar
  2. 2.
    Xie, X., Mirmehdi, M.: A galaxy of texture features. In: Handbook of Texture Analysis, pp. 375–406. World Scientific, Singapore (2008)CrossRefGoogle Scholar
  3. 3.
    Haralick, R.M., Shanmugam, K., et al.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  4. 4.
    Cross, G.R., Jain, A.K.: Markov random field texture models. IEEE Trans. Pattern Anal. Mach. Intell. 5(1), 25–39 (1983)CrossRefGoogle Scholar
  5. 5.
    Chang, T., Kuo, C.-C.J.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans Image Process. 2(4), 429–441 (1993)CrossRefGoogle Scholar
  6. 6.
    Idrissa, M., Acheroy, M.: Texture classification using gabor filters. Pattern Recognit. Lett. 23(9), 1095–1102 (2002)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Dubey, S.R., Singh, S.K., Singh, R.K.: Local wavelet pattern: a new feature descriptor for image retrieval in medical ct databases. IEEE Trans. Image Process. 24(12), 5892–5903 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Dubey, S.R., Singh, S.K., Singh, R.K.: Local bit-plane decoded pattern: a novel feature descriptor for biomedical image retrieval. IEEE J. Biomed. Health Inform. 20(4), 1139–1147 (2016)CrossRefGoogle Scholar
  10. 10.
    Dubey, S.R., Singh, S.K., Singh, R.K.: Local diagonal extrema pattern: a new and efficient feature descriptor for ct image retrieval. IEEE Signal Proces. Lett. 22(9), 1215–1219 (2015)CrossRefGoogle Scholar
  11. 11.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. 19(2), 533–544 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Murala, S., Maheshwari, R.P., Balasubramanian, R.: Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans. Image Process. 21(5), 2874–2886 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Dubey, S.R., Singh, S.K., Singh, R.K.: Multichannel decoded local binary patterns for content-based image retrieval. IEEE Trans. Image Process. 25(9), 4018–4032 (2016)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Wang, X., Han, T.X., Yan, S.: An hog-lbp human detector with partial occlusion handling. In: ICCV, pp. 32–39. IEEE (2009)Google Scholar
  16. 16.
    Dubey, S.R., Singh, S.K., Singh, R.K.: Rotation and illumination invariant interleaved intensity order-based local descriptor. IEEE Trans. Image Process. 23(12), 5323–5333 (2014)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Guo, Z., Zhang, L., Zhang, D.: Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognit. 43(3), 706–719 (2010)CrossRefGoogle Scholar
  19. 19.
    Liu, L., Long, Y., Fieguth, P.W., Lao, S., Zhao, G.: Brint: binary rotation invariant and noise tolerant texture classification. IEEE Trans. Image Process. 23(7), 3071–3084 (2014)Google Scholar
  20. 20.
    Roy, S.K., Bhattacharya, N., Chanda, B., Chaudhuri, B.B., Ghosh, D.K.: FWLBP: a scale invariant descriptor for texture classification. arXiv:1801.03228 (2018)
  21. 21.
    Roy, S.K., Chanda, B., Chaudhuri, B., Ghosh, D.K., Dubey, S.R.; A complete dual-cross pattern for unconstrained texture classification. In: 4th Asian Conference on Pattern Recognition (ACPR 2017), Nanjing, China, pp. 741–746 (2017)Google Scholar
  22. 22.
    Roy, S.K., Chanda, B., Chaudhuri, B.B., Banerjee, S., Ghosh, D.K., Dubey, S.R.: Local jet pattern: a robust descriptor for texture classification. arXiv:1711.10921 (2017)
  23. 23.
    Roy, S.K., Chanda, B., Chaudhuri, B.B., Banerjee, S., Ghosh, D.K., Dubey, S.R.: Local directional zigzag pattern: a rotation invariant descriptor for texture classification. Pattern Recognit. Lett. 108, 23–30 (2018)CrossRefGoogle Scholar
  24. 24.
    Roy, S.K., Chanda, B., Chaudhuri, B.B., Ghosh, D.K., Dubey, S.R.: Local morphological pattern: a scale space shape descriptor for texture classification. Digit. Signal Process. (In Press) (2018). ElsevierGoogle Scholar
  25. 25.
    Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press, Cambridge (2016)Google Scholar
  26. 26.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)Google Scholar
  27. 27.
    Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248–255. IEEE (2009)Google Scholar
  28. 28.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
  29. 29.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., et al.: Going deeper with convolutions. In: CVPR (2015)Google Scholar
  30. 30.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)Google Scholar
  31. 31.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)Google Scholar
  32. 32.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: ICCV, pp. 2980–2988. IEEE (2017)Google Scholar
  33. 33.
    Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.-W., Snead, D.R.J., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016)CrossRefGoogle Scholar
  34. 34.
    Hafemann, L.G., Oliveira, L.S., Cavalin, P.R., Sabourin, R.: Transfer learning between texture classification tasks using convolutional neural networks. In: IJCNN, pp. 1–7. IEEE (2015)Google Scholar
  35. 35.
    Basu, S., Mukhopadhyay, S., Karki, M., DiBiano, R., Ganguly, S., Nemani, R., Gayaka, S.: Deep neural networks for texture classificationa theoretical analysis. Neural Netw. 97, 173–182 (2018)CrossRefGoogle Scholar
  36. 36.
    Andrearczyk, V., Whelan, P.F.: Using filter banks in convolutional neural networks for texture classification. Pattern Recognit. Lett. 84, 63–69 (2016)CrossRefGoogle Scholar
  37. 37.
    Cimpoi, M., Maji, S., Vedaldi, A.: Deep filter banks for texture recognition and segmentation. In: CVPR, pp. 3828–3836 (2015)Google Scholar
  38. 38.
    Liu, L., Fieguth, P., Wang, X., Pietikäinen, M., Hu, D.: Evaluation of lbp and deep texture descriptors with a new robustness benchmark. In: ECCV, pp. 69–86. Springer, Berlin (2016)CrossRefGoogle Scholar
  39. 39.
    Bruna, J., Mallat, S.: Invariant scattering convolution networks. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1872–1886 (2013)CrossRefGoogle Scholar
  40. 40.
    Sifre, L., Mallat, S.: Rotation, scaling and deformation invariant scattering for texture discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1233–1240 (2013)Google Scholar
  41. 41.
    Chan, T.-H., Jia, K., Gao, S., Jiwen, L., Zeng, Z., Ma, Y.: Pcanet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017–5032 (2015)MathSciNetCrossRefGoogle Scholar
  42. 42.
    Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover publications, New York (1966)Google Scholar
  43. 43.
    Dana, K.J., Van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. ACM TOG 18(1), 1–34 (1999)CrossRefGoogle Scholar
  44. 44.
    Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the significance of real-world conditions for material classification. In: European Conference on Computer Vision, pp. 253–266. Springer, Berlin (2004)Google Scholar
  45. 45.
    Liao, S., Law, M.W.K., Chung, A.C.S.: Dominant local binary patterns for texture classification. IEEE Trans. Image Process. 18(5), 1107–1118 (2009)MathSciNetCrossRefGoogle Scholar
  46. 46.
    Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vis. 62(1–2), 61–81 (2005)CrossRefGoogle Scholar
  47. 47.
    Qi, X., Xiao, R., Chun-Guang Li, Y., Qiao, J.G., Tang, X.: Pairwise rotation invariant co-occurrence local binary pattern. IEEE TPAMI 36(11), 2199–2213 (2014)CrossRefGoogle Scholar
  48. 48.
    Nosaka, R., Suryanto, C.H., Fukui, K.: Rotation invariant co-occurrence among adjacent lbps. In: ACCV, pp. 15–25. Springer, Berlin (2012)CrossRefGoogle Scholar
  49. 49.
    Duan, Y., Jiwen, L., Feng, J., Zhou, J.: Learning rotation-invariant local binary descriptor. IEEE Trans. Image Process. 26(8), 3636–3651 (2017)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Jalpaiguri Government Engineering CollegeJalpaiguriIndia
  2. 2.Indian Institute of Information TechnologySri CityIndia
  3. 3.Indian Statistical InstituteKolkataIndia
  4. 4.Adamas UniversityKolkataIndia

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