Skip to main content
SpringerLink
Log in

Geometrically modeled derivative feature descriptor aiding supervised shape retrieval

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods.

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
Fig. 12

Similar content being viewed by others

References

  1. Fabio AD, Gamba P (1998) Simplified modal analysis and search for reliable shape retrieval. IEEE Trans Circuit Syst Video Technol 8:656–666

    Article  Google Scholar 

  2. Celik C, Bilge HS (2017) Content based image retrieval with sparse representations and local feature descriptors: a comparative study. Pattern Recogn 68:1–13

    Article  Google Scholar 

  3. Zhou W, Li H, Tian Q (2017) Recent advance in content-based image retrieval: a literature survey. arXiv:1706.06064

  4. Sardey Mohini P, Kharate GK (2015) A comparative analysis of retrieval techniques in content based image retrieval. IETE J Res. arXiv:1508.06728

  5. Borras A, Llados J (2005) Object image retrieval by shape content in complex scenes using geometric constraints. Pattern Recognition Image Analysis. Springer, Berlin, pp 325–332

    Google Scholar 

  6. Rui Y, She AC, Huang TS (1997) A modified fourier descriptor for shape matching in mars, series on software engineering and knowledge engineering, vol 8. World Scientific Publishing, Singapore, pp 165–180

    Google Scholar 

  7. Bartolini I, Ciaccia P, Patella M (2005) WARP: accurate retrieval of shapes using phase of fourier descriptors and time warping distance. IEEE Trans Pattern Anal Mach Intell 27:142–1476

    Article  Google Scholar 

  8. Mokhtarian F, Abbasi S, Kittler J (1997) Efficient and robust retrieval by shape content through curvature scale space, vol 8. Image Databases and Multi-Media Search, Danvers, pp 51–58

    Google Scholar 

  9. Attalia E, Siy P (2005) Robust shape similarity retrieval based on contour segmentation polygonal multi resolution and elastic matching. Pattern Recogn 38:2229–2241

    Article  Google Scholar 

  10. Shu X, Wu XJ (2011) A novel contour descriptor for 2D shape matching and its application to image retrieval. Image Vis Comput 29:286–294

    Article  Google Scholar 

  11. Ling H, Okada K (2007) An efficient Earth mover’s distance algorithm for robust histogram comparison. IEEE Trans Pattern Anal Mach Intell 29:840–853

    Article  Google Scholar 

  12. Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24:509–522

    Article  Google Scholar 

  13. Ling H, Jacobs DW (2007) Shape classification using the inner distance. IEEE Trans Pattern Anal Mach Intell 29:286–299

    Article  Google Scholar 

  14. Xu CJ, Liu JZ, Tang X (2009) 2D shape matching by contour flexibility. IEEE Trans Pattern Anal Mach Intell 31:180–186

    Article  Google Scholar 

  15. Xu C, Liu J, Tang X (2009) 2D shape matching by contour flexibility. IEEE Trans Pattern 15 Anal Mach Intell 31(1):180–186

    Article  Google Scholar 

  16. El R, Ibrahim NA, Kamel M, Ahmed M, Freeman G (2005) Robust multi-scale triangle-area representation for 2D shapes. In: Proceedings of the IEEE international conference on image processing, vol 1, Genoa, Italy, pp I–545

  17. Alajlan N, El Rube I, Kamel MS, Freeman G (2007) Shape retrieval using triangle-area representation and dynamic space warping. Pattern Recogn 40:1911–1920

    Article  Google Scholar 

  18. Temlyakov A, Munsell BC, Waggoner JW, Wang S (2010) Two perceptually motivated strategies for shape classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 16, San Francisco, pp 2289–2296

  19. Wang J, Bai X, You X, Liu W, Jan Latecki L (2012) Shape matching and classification using height functions. Pattern Recogn Lett 33(2):134–143

    Article  Google Scholar 

  20. McNeill G, Vijayakumar S (2006) Hierarchical procrustes matching for shape retrieval. In: Proceedings of the IEEE conference on CVPR. New York, USA, pp 885–894

  21. Yang X, Koknar-Tezel S, Latecki LJ (2009) Locally constrained diffusion process on locally densied distance spaces with applications to shape retrieval. In: Proceedings of the IEEE conference on CVPR. Miami, pp 357–364

  22. Hu R, Jia W, Ling H, Zhao Y, Gui J (2014) Angular pattern and binary angular pattern for shape retrieval. IEEE Trans Image Process 23(3):1118–1127

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  24. Wang B, Gao YS (2014) Hierarchical string cuts: a translation, rotation, scale, and mirror invariant descriptor for fast shape retrieval. IEEE Trans Image Process 23(9):4101–4111

    Article  MathSciNet  Google Scholar 

  25. Matsuda Y, Ogawa M, Yano M (2015) Shape retrieval with geometrically characterized contour partitions access. IEEE 3:1161–1178

    Google Scholar 

  26. Hu D, Huang W, Yang J, Shang L, Zhu Z (2015) Shape matching and object recognition using common base triangle area. Comput Vis, IET 9(5):769–778

    Article  Google Scholar 

  27. Yang J, Wang H, Yuan J, Li Y, Liu J (2016) Invariant multi-scale descriptor for shape representation, matching and retrieval. Comput Vis Image Understand 145:43–58

    Article  Google Scholar 

  28. Castellano G, Fanelli AM, Sforza G, Alessandra Torsello M (2016) Shape annotation for intelligent image retrieval. Appl Intell 44(1):179–195

    Article  Google Scholar 

  29. Freitas AM, Torres RS, Paulo AVM (2016) TSB: tensor scale descriptors within circular sectors for fast shape retrieval. Pattern Recogn Lett 83:303–311

    Article  Google Scholar 

  30. Kaothanthong N, Chun J, ratio Takeshi T (2016) Distance interior a new shape signature for 2D shape retrieval. Pattern Recogn Lett 78:14–21

    Article  Google Scholar 

  31. Kundu MK, Chowdhury M, Bulò SR (2015) A graph-based relevance feedback mechanism in content-based image retrieval. Knowl-Based Syst 73:254–264

    Article  Google Scholar 

  32. Chen X (2005) Neural network-based shape retrieval using moment invariants and Zernike moments, Electronic theses and dissertations.2824 university of windsor. http://scholar.uwindsor.ca/etd/2824

  33. Seetharaman K, Sathiamoorthy S (2015) Color image retrieval using statistical model and radial basis function neural network. Egyptian Inform J 15(1):59–68

    Article  Google Scholar 

  34. Varga D, Szirányi T (2016) Fast content-based image retrieval using convolutional neural network and hash function. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC). Budapest, pp 002636–002640

  35. Karamti H, Mohamed T, Gargouri F (2014) Content-based image retrieval system using neural network. In: 2014 IEEE/ACS 11th international conference on computer systems and applications (AICCSA). Doha, pp 723–728

  36. Latecki LJ, Lakamper R (2000) Shape similarity measure based on correspondence of visual parts. IEEE Trans Pattern Anal Mach Intell 22(10):1185–1190

    Article  Google Scholar 

  37. Grigorescu C, Petkov N (2003) Distance sets for shape filters and shape recognition. IEEE Trans Image Process 12(10):1274–1286

    Article  MathSciNet  Google Scholar 

  38. Adamek T, O’Connor NE (2004) A multi-scale representation method for non-rigid shapes with a single closed contour. IEEE Trans Circuit Syst Video Technol 14(5):742–743

    Article  Google Scholar 

  39. PalazóN-GonzáLez V, Marzal A (2012) On the dynamic time warping of cyclic sequences for shape 7 retrieval. Image Vis Comput 30(12):978–90

    Article  Google Scholar 

  40. Ling H, Yang X, Latecki LJ (2010) Balancing deformability and discriminability for shape matching. In: Proceedings of the European conference on computer vision. Springer, Berlin, pp 411–424

    Chapter  Google Scholar 

  41. Bai X, Yang X, Latecki L, Liu W (2010) Learning context-sensitive shape similarity by graph transduction. IEEE Trans 32(5):861–874

    Google Scholar 

  42. Bai X, Wang B, Yao C, Liu W, Tu Z (2012) Co-transduction for shape retrieval. IEEE Trans Image Process 21(5):2747–2757

    Article  MathSciNet  Google Scholar 

  43. Ion A, Artner NM, Peyré G, Kropatsch WG, Cohen L Matching 2D & 3D Articulated Shapes using Eccentricity

  44. Bai X, Rao C, Wang X (2014) Shape vocabulary: a robust and 1 efficient shape representation for shape 2 matching. IEEE Trans Image Process 23(9):3935–49

    Article  MathSciNet  Google Scholar 

  45. Felzenszwalb PF, Schwartz JD (2007) Hierarchical matching of deformable shapes. In: IEEE conference on computer 7 vision and pattern recognition, 2007. CVPR’07, vol 17, pp 1–8

Download references

Author information

Authors and Affiliations

  1. School of Electronics Engineering, VIT, Vellore, 632014, India

    Priyanka S & Sudhakar M S

Authors
  1. Priyanka S
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sudhakar M S
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Priyanka S.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

S, P., M S, S. Geometrically modeled derivative feature descriptor aiding supervised shape retrieval. Appl Intell 48, 4960–4975 (2018). https://doi.org/10.1007/s10489-018-1251-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-018-1251-x

Keywords

Search

Navigation