Combining Local Binary Pattern and Speeded-Up Robust Feature for Content-Based Image Retrieval

  • Prashant SrivastavaEmail author
  • Manish Khare
  • Ashish Khare
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1178)


Large number of digital image libraries containing huge amount of images have made the task of searching and retrieval tedious. Content-Based Image Retrieval (CBIR) is a field which finds solution to this problem. This paper proposes CBIR a technique which extracts interest points from texture feature at multiple resolutions of image. Local Binary Pattern (LBP) has been used to perform texture feature extraction and interest points are gathered through Speeded-Up Robust Feature (SURF) descriptors. The multiresolution decomposition of image is done using Discrete Wavelet Transform (DWT). DWT coefficients of gray scale image are computed followed by computation of LBP codes of resulting DWT coefficients. The interest points from texture image are then gathered by computing SURF descriptors of resulting LBP codes. Finally, feature vector for retrieval is constructed through Gray-Level Co-occurrence Matrix (GLCM) which is used to retrieve visually similar images. The performance of the proposed method has been tested on Corel-1 K dataset and measured in terms of precision and recall. The experimental results demonstrate that the proposed method performs better than some of the other state-of-the-art CBIR techniques in terms of precision and recall.


Content-Based Image Retrieval Local Binary Pattern Speeded-Up Robust Feature Gray-Level Co-occurrence matrix 


  1. 1.
    Dutta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: ideas influences and trends of the new age. ACM Comput. Surv. 40(2), 1–60 (2008)CrossRefGoogle Scholar
  2. 2.
    Smith, J.R., Chang, S.F.: Tools and Techniques for Color Image Retrieval. Electronic Imaging, Science and Technology. International Society for Optics and Photonics 2670, 426–437 (1996)Google Scholar
  3. 3.
    Wang, X., Yu, Y., Yang, H.: An effective image retrieval scheme using color, texture and shape features. Comput. Stand. Interfaces 33(1), 59–68 (2011)CrossRefGoogle Scholar
  4. 4.
    Srivastava, P., Binh, N.T., Khare, A.: Content-based image retrieval using moments. In: Proceedings of Context-Aware Systems and Applications, Phu Quoc, Vietnam, pp. 228–237 (2013)Google Scholar
  5. 5.
    Srivastava, P., Binh, N.T., Khare, A.: Content-based image retrieval using moments of local ternary pattern. Mob. Netw. Appl. 19, 618–625 (2014)CrossRefGoogle Scholar
  6. 6.
    Youssef, S.M.: ICTEDCT-CBIR: integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval. Comput. Electr. Eng. 38, 1358–1376 (2012)CrossRefGoogle Scholar
  7. 7.
    Srivastava, P., Khare, A.: Integration of wavelet transform, local binary pattern, and moments for content-based image retrieval. J. Vis. Commun. Image Represent. 42(1), 78–103 (2017)CrossRefGoogle Scholar
  8. 8.
    Liu, G.H., Yang, J.Y.: Content-based image retrieval using color difference histogram. Pattern Recogn. 46(1), 188–198 (2013)CrossRefGoogle Scholar
  9. 9.
    Zhang, M., Zhang, K., Feng, Q., Wang, J., Jun, K., Lu, Y.: A novel image retrieval method based on hybrid information descriptors. J. Vis. Commun. Image Represent. 25(7), 1574–1587 (2014)CrossRefGoogle Scholar
  10. 10.
    Wang, X., Wang, Z.: A novel method for image retrieval based on structure elements descriptor. J. Vis. Commun. Image Represent. 24(1), 63–74 (2013)CrossRefGoogle Scholar
  11. 11.
    Liu, G., Zhang, L., Hou, Y., Yang, J.: Image retrieval based on multi-texton histogram. Pattern Recogn. 43(7), 2380–2389 (2008)CrossRefGoogle Scholar
  12. 12.
    Liu, G., Li, Z., Zhang, L., Xu, Y.: Image retrieval based on microstructure descriptor. Pattern Recogn. 44(9), 2123–2133 (2011)CrossRefGoogle Scholar
  13. 13.
    Feng, L., Wu, J., Liu, S., Zhang, H.: Global correlation descriptor: a novel image representation for image retrieval. J. Vis. Commun. Image Represent. 33, 104–114 (2015)CrossRefGoogle Scholar
  14. 14.
    Xia, Yu., Wan, S., Jin, P., Yue, L.: Multi-scale local spatial binary patterns for content-based image retrieval. In: Yoshida, T., Kou, G., Skowron, A., Cao, J., Hacid, H., Zhong, N. (eds.) AMT 2013. LNCS, vol. 8210, pp. 423–432. Springer, Cham (2013). Scholar
  15. 15.
    Srivastava, P., Khare, A.: Content-based image retrieval using multiscale local spatial binary Gaussian co-occurrence pattern. In: Hu, Y.-C., Tiwari, S., Mishra, Krishn K., Trivedi, Munesh C. (eds.) Intelligent Communication and Computational Technologies. LNNS, vol. 19, pp. 85–95. Springer, Singapore (2018). Scholar
  16. 16.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall Press, Upper Saddle River (2002)Google Scholar
  17. 17.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  18. 18.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). Scholar
  19. 19.
    Haralick, R.M., Shanmungam, K., Dinstein, I.: Textural features of image classification. IEEE Trans. Syst. Man Cybern. B Cybern. 3, 610–621 (1973)CrossRefGoogle Scholar
  20. 20.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Prashant Srivastava
    • 1
    Email author
  • Manish Khare
    • 2
  • Ashish Khare
    • 3
  1. 1.NIIT UniversityRajasthanIndia
  2. 2.Dhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia
  3. 3.Department of Electronics and CommunicationUniversity of AllahabadAllahabadIndia

Personalised recommendations