Skip to main content
SpringerLink
Log in

Efficient and distinctive binary descriptor for rotated circular image recognition

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Based on pairs of spatial symmetric patches, a novel efficient and distinctive binary descriptor is proposed in this paper for rotated circular image recognition. To achieve rotation invariance during feature computation, a local coordinate system is first found with radial transform technology. On the basis of that, local binary patterns against rotation can be extracted. Meanwhile, the circular image is divided into a set of overlapped annular regions, and pairs of patch description are constructed with histogram within the ring. Finally, the rotation-invariant image description can be obtained by concatenating the ring features from inner to outer. The performance of proposed method is tested with three datasets, i.e., the euro coins, QQ expression and car logo. The test results show that its recognition accuracies reach 100, 100 and 97.07%, respectively, which are superior to the results of traditional methods based on LBP feature. And our method presents competitive performance contrasted to recently proposed LBP invariants, i.e., R_LBP, CS_LBP and PRI_COLBP, and conventional floating and binary descriptors, such as SIFT, SURF, BRISK and FREAK. Moreover, the algorithm is efficient, as single-point feature computation needed only 0.045 ms.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Bay H, Tuytelaars T, Van Gool L, et al.: SURF: speeded up robust features. In: European Conference on Computer Vision, 2006, pp. 404–417

  3. Fan, B., Wu, F., Hu, Z., et al.: Rotationally invariant descriptors using intensity order pooling. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 2031–2045 (2012)

    Article  Google Scholar 

  4. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005, pp. 886–893

  5. Chandrasekhar, V., Takacs, G., Chen, D., et al. CHoG: Compressed histogram of gradients A low bit-rate feature descriptor. In: Computer Vision and Pattern Recognition, 2009, pp. 2504–2511

  6. Wang, Z., Fan, B., Wu, F., et al.: Local intensity order pattern for feature description. In: International Conference on Computer Vision, 2011, pp. 603–610

  7. Alcantarilla, P.F., Nuevo, J., Bartoli, A., et al.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. Br. Mach. Vis. Conf. 13(1-13), 11 (2013)

    Google Scholar 

  8. Song, K., Yan, Y., Chen, W., et al.: Research and perspective on local binary pattern. Acta Autom. Sinica 39(6), 730–744 (2013)

    Article  Google Scholar 

  9. Calonder, M., Lepetit, V., Ozuysal, M., et al.: BRIEF: computing a local binary descriptor very fast. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2012)

    Article  Google Scholar 

  10. Leutenegger S, Chli M, Siegwart R, et al. BRISK: binary robust invariant scalable keypoints. In: International Conference on Computer Vision, 2011, pp. 2548–2555

  11. Rublee E, Rabaud V, Konolige K, et al. ORB: An efficient alternative to SIFT or SURF. In: International Conference on Computer Vision, 2011, pp. 2564–2571

  12. Alahi A, Ortiz R, Vandergheynst P, et al. FREAK: fast retina keypoint. In: Computer Vision and Pattern Recognition, 2012, pp. 510–517

  13. Wang, Z., Fan, B., Wu, F., et al.: FRIF: fast robust invariant feature. Br. Mach. Vis. Conf. 16(1-16), 11 (2013)

    Google Scholar 

  14. Xu, X., Tian, L., Feng, J., et al.: OSRI: a rotationally invariant binary descriptor. IEEE Trans. Image Process. 23(7), 2983–2995 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  15. Yang, X., Cheng, K.T.: Local difference binary for ultrafast and distinctive feature description. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 188–194 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Guan, W., You, S.: Robust image matching with line context. Br. Mach. Vis. Conf. 34(1-34), 11 (2013)

    Google Scholar 

  18. Takacs, G., Chandrasekhar, V., Tsai, S.S., et al.: Unified real-time tracking and recognition with rotation-invariant fast features. In: Computer Vision and Pattern Recognition, 2010, pp. 934–941

  19. Ojala, T., PietikÄainen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  20. PietikÄainen, M., Ojala, T., Xu, Z.: Rotation-invariant texture classification using feature distributions. Pattern Recogn. 33(1), 43–52 (2000)

    Article  Google Scholar 

  21. Ojala, T., Valkealahti, K., Oja, E., PietikÄainen, M.: Texture discrimination with multidimensional distributions of signed gray-level differences. Pattern Recogn. 34(3), 727–739 (2001)

    Article  MATH  Google Scholar 

  22. Fathi, A., Naghsh-Nilchi, A.R.: Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recogn. Lett. 33(9), 1093–1100 (2012)

    Article  Google Scholar 

  23. Guo, Z.H., 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 

  24. Nguyen, D.T., Ogunbona, P.O., Li, W.: A novel shape-based non-redundant local binary pattern descriptor for object detection. Pattern Recogn. 46(5), 1485–1500 (2013)

    Article  Google Scholar 

  25. Valev, Ventzeslav: From binary features to non-reducible descriptors in supervised pattern recognition problems. Pattern Recogn. Lett. 45(1), 106–114 (2014)

    Article  Google Scholar 

  26. Guo, Y.M., Zhao, G.Y., PietikÄainen, M.: Discriminative features for texture description. Pattern Recogn. 45(10), 3834–3843 (2012)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  28. Shan, S., Zhang, W., Su, Y., et al.: Ensemble of piecewise FDA based on spatial histograms of local (Gabor) binary patterns for face recognition. In: International Conference on Computer Vision, 2006, pp. 606–609

  29. Zhang, S., Tian, Q., Huang, Q., et al.: USB: ultrashort binary descriptor for fast visual matching and retrieval. IEEE Trans. Image Process. 23(8), 3671–3683 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  30. Roy, A., Marcel, S.: Haar local binary pattern feature for fast illumination invariant face detection. In: British Machine Vision Conference, 2009

  31. Trzcinski, T., Lepetit, V.: Efficient discriminative projections for compact binary descriptors. In: European Conference on Computer Vision, 2012, pp. 228–242

  32. Strecha, C., Bronstein, A.M., Bronstein, M.M., et al.: LDAHash: improved matching with smaller descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 34(1), 66–78 (2012)

    Article  Google Scholar 

  33. Gong, Y., Lazebnik, S., Gordo, A., et al.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916–2929 (2013)

    Article  Google Scholar 

  34. Trzcinski, T., Christoudias, M., Lepetit, V., et al.: Learning image descriptors with boosting. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 597–610 (2015)

    Article  Google Scholar 

  35. HeikkilÄa, M., PietikÄainen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recogn. 42(3), 425–436 (2009)

    Article  Google Scholar 

  36. Zhao, Y., Jia, W., Hu, R.X., Min, H.: Completed robust local binary pattern for texture classification. Neurocomputing 106(4), 68–76 (2013)

    Article  Google Scholar 

  37. Qi, X.B., Xiao, O.R., Li, C.G., Qiao, Y., Guo, J., Tang, X.: Pairwise Rotation Invariant Co-occurrence Local Binary Pattern. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2199–2213 (2014)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant Number 61602397], the Natural Science Foundation of Hunan Province [Grant Numbers 2017JJ2251, 2017JJ3315] and the Key Discipline Construction Project of Hunan Province.

Author information

Authors and Affiliations

  1. The College of Information Engineering, Xiangtan University, Xiangtan, China

    Dongbo Zhang, Honglei Chen, Feng Yin, Zhiqiang Chen, Hongzhong Tang & Haixia Xu

  2. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan, China

    Dongbo Zhang, Feng Yin & Haixia Xu

Authors
  1. Dongbo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Honglei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Feng Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiqiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hongzhong Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Haixia Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dongbo Zhang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, D., Chen, H., Yin, F. et al. Efficient and distinctive binary descriptor for rotated circular image recognition. Machine Vision and Applications 30, 749–761 (2019). https://doi.org/10.1007/s00138-019-01026-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-019-01026-8

Keywords

Search

Navigation