Skip to main content
SpringerLink
Log in

Retrieving 2D shapes by similarity based on bag of salience points

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper proposes a novel shape feature description based on salient points, called Bag-of-Salience-Points (BoSP). The proposed descriptor is compact and provides a fast solution for finding the correspondences of two set of salient points, contributing to speed-up the task of shape matching. The novelty of the proposed descriptor lies in combining local sparse features (salient points) encoded in global and spatial-based histograms with a few other shape factors like eccentricity. The proposed shape descriptor retrieves the best matching, even in occlusions situations, where points in the two shapes cannot be properly matched. The BoSP is validated on several benchmark datasets for 2D shape matching algorithms, and it is observed that the BoSP maintains superior discriminative, while being invariant to geometric transformations as well as demanding a low computational cost to measure the similarity of shapes.

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

Similar content being viewed by others

References

  1. Alajlan N, Kamel MS, Freeman GH (2008) Geometry-based image retrieval in binary image databases. IEEE Transactions on Pattern Analysis and Machine Intelligence 30:1003–1013

    Article  Google Scholar 

  2. Andaló F, Miranda P, da S. Torres R, Falcão A (2010) Shape feature extraction and description based on tensor scale. Pattern Recogn 43(1):26–36

    Article  MATH  Google Scholar 

  3. Arica N, Vural FTY (2003) Bas: a perceptual shape descriptor based on the beam angle statistics. Pattern Recogn Lett 24(9–10):1627–1639

    Article  MATH  Google Scholar 

  4. Arulmozhi P, Abirami S (2014) Shape based image retrieval: a review. Int J Comp Sci Eng 6:14–153

    Google Scholar 

  5. Bai X, Latecki LJ (2008) Path similarity skeleton graph matching. IEEE TRANS PAMI 30(7)

  6. Bai X, Rao C, Wang X (2014) Shape vocabulary: a robust and efficient shape representation for shape matching. IEEE Trans Image Process 23(9):3935–3949

    Article  MathSciNet  Google Scholar 

  7. Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24:509–522

    Article  Google Scholar 

  8. Bicego M, Murino V (2004) Investigating hidden markov models’ capabilities in 2d shape classification. IEEE Trans Pattern Anal Mach Intell 26(2):281–286

    Article  Google Scholar 

  9. Biswas S, Aggarwal G, Chellappa R (2010) An efficient and robust algorithm for shape indexing and retrieval. IEEE Trans Multimed 12:372–385

    Article  Google Scholar 

  10. Bober M (2001) Mpeg-7 visual shape descriptors. IEEE Transactions on Circuits and Systems for Video Technology 11(6):716–719

    Article  Google Scholar 

  11. Chen Y, Shi L, Feng Q, Yang J, Shu H, Luo L, Coatrieux JL, Chen W (2014) Artifact suppressed dictionary learning for low-dose ct image processing. IEEE Trans Med Imaging 33(12):2271–2292

    Article  Google Scholar 

  12. Daliri MR, Torre V (2008) Robust symbolic representation for shape recognition and retrieval. Pattern Recogn 5:1782–1798

    Article  MATH  Google Scholar 

  13. Dalitz C, Brandt C, Goebbels S, Kolanus D (2013) Fourier descriptors for broken shapes. EURASIP J Adv Signal Proc:161–161

  14. Ding C, Xu C, Tao D (2015) Multi-task pose-invariant face recognition. IEEE Trans Image Process 24(3):980–993

    Article  MathSciNet  Google Scholar 

  15. Felzenszwalb PF, Schwartz JD (2007) Hierarchical matching of deformable shapes. IEEE Conference on Computer Vision and Pattern Recognition (C:1–8

  16. Hu R, Barnard M, Collomosse J (2010) Gradient field descriptor for sketch based retrieval and localization. In: IEEE International conference on image processing (ICIP), pp 1025–1028

  17. Khotanzad A, Hong YH (1990) Invariant image recognition by zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 12:489–497

    Article  Google Scholar 

  18. Klein T, Sebastian PN, Kimia BB (2004) Recognition of shapes by editing their shock graphs. IEEE Trans Pattern Anal Mach Intell 26:550–571

    Article  Google Scholar 

  19. Kovács A, Szirányi T (2009) Local contour descriptors around scale-invariant keypoints. In: 2009 16th IEEE international conference on image processing (ICIP), pp 1105–1108

  20. Latecki LJ, Lakamper R (2000) Shape similarity measure based on correspondence of visual parts. IEEE Transactions on Pattern Analysis and Machine Intelligence 22:1185–1190

    Article  Google Scholar 

  21. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Conference on computer vision and pattern recognition, pp 2169–2178

  22. Li Z, Qu W, Cao J, Su Z (2012) Geometry-based 2d shape descriptor for retrieval in large database. IEEE International Conference on Signal Processing 2:1096–1101

    Google Scholar 

  23. Ling H, Jacobs DW (2007) Shape classification using the inner-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 29:286–299

    Article  Google Scholar 

  24. Liu J, Wang S (2015) Salient region detection via simple local and global contrast representation. Neurocomputing 147:435–443

    Article  Google Scholar 

  25. Liu M, Vemuri BC, Amari S, Nielsen F (2010) Total bregman divergence and its applications to shape retrieval. In: IEEE conference on computer vision and pattern recognition. CVPR, pp 3463–3468

  26. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  27. Mei Y, Androutsos D (2009) Robust affine invariant region-based shape descriptors: The ica zernike moment shape descriptor and the whitening zernike moment shape descriptor. IEEE Signal Process Lett 16(10):877–880. doi:10.1109/LSP.2009.2026119

  28. Mokhtarian F, Suomela R (1998) Robust image corner detection through curvature scale space. IEEE Transactions on Pattern Analysis and Machine Intelligence 20:1376–1381

    Article  Google Scholar 

  29. Pedrosa GV, Barcelos CA (2010) Anisotropic diffusion for effective shape corner point detection. Pattern Recogn Lett 31(12):1658–1664

    Article  Google Scholar 

  30. Qin C, Chang CC, Tsou PL (2013) Robust image hashing using non-uniform sampling in discrete fourier domain. Digital Signal Processing 23(2):578–585

    Article  MathSciNet  Google Scholar 

  31. Qin C, Chen X, Ye D, Wang J, Sun X (2016) A novel image hashing scheme with perceptual robustness using block truncation coding. Inf Sci 361–362:84–99

    Article  Google Scholar 

  32. Schmidt FR, Töppe E, Cremers D (2009) Efficient planar graph cuts with applications in computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 351–356

  33. Sivic J, Zisserman A (2003) Video google: a text retrieval approach to object matching in videos. In: IEEE International conference on computer vision, pp 1470–1477

  34. Thakoor N, Gao J, Jung S (2007) Hidden markov model-based weighted likelihood discriminant for 2-d shape classification. IEEE Trans Image Process 16 (11):2707–2719

    Article  MathSciNet  Google Scholar 

  35. Torres R, Falcao A, da F, Costa L (2003) A graph-based approach for multiscale shape analysis. Pattern Recogn 37(6):1163–1174

    Article  Google Scholar 

  36. Tu Z, Yuille AL (2004) Shape matching and recognition using generative models and informative features. European Conference on Computer Vision (ECCV) 3:195–209

    MATH  Google Scholar 

  37. Wang Z, Liang M (2010) Locally affine invariant descriptors for shape matching and retrieval. Signal Process Lett 17:803–806

    Article  Google Scholar 

  38. Wang J, Bai X, You X, Liu W, Latecki LJ (2012) Shape matching and classification using height functions. Pattern Recogn Lett 33:134–143

    Article  Google Scholar 

  39. Xu C, Liu J, Tang X (2009) 2d shape matching by contour flexibility. IEEE Transactions on Pattern Analysis and Machine Intelligence 31:180–186

    Article  Google Scholar 

  40. You J, Bhattacharya P (2000) Dynamic shape retrieval by hierarchical curve matching, snakes and data mining. In: Proceedings 15th international conference on pattern recognition, 2000, vol 1

  41. Zhao G, Pietikainen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 29(6):915–928

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported, in part, by FAPESP, CNPq, CAPES, SticAMSUD, the RESCUER project, funded by the European Commission (Grant 614154) and by the Brazilian National Council for Scientific and Technological Development CNPq/MCTI.

Author information

Authors and Affiliations

  1. Universidade Estadual de Goias (UEG), Santa Helena, GO, Brazil

    Glauco V. Pedrosa

  2. University of Sao Paulo (USP), Sao Carlos, SP, Brazil

    Agma J. M. Traina

  3. Federal University of Uberlandia, Uberlandia, MG, Brazil

    Celia A. Z. Barcelos

Authors
  1. Glauco V. Pedrosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Agma J. M. Traina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Celia A. Z. Barcelos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Glauco V. Pedrosa.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pedrosa, G.V., Traina, A.J.M. & Barcelos, C.A.Z. Retrieving 2D shapes by similarity based on bag of salience points. Multimed Tools Appl 76, 20957–20971 (2017). https://doi.org/10.1007/s11042-016-4046-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4046-2

Keywords

Search

Navigation