Applied Intelligence

, Volume 48, Issue 12, pp 4960–4975 | Cite as

Geometrically modeled derivative feature descriptor aiding supervised shape retrieval

  • Priyanka S
  • Sudhakar M S


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.


Classification Law of sines Shape descriptor Tetrakis square tiling Triangulated second-order shape derivative 


  1. 1.
    Fabio AD, Gamba P (1998) Simplified modal analysis and search for reliable shape retrieval. IEEE Trans Circuit Syst Video Technol 8:656–666CrossRefGoogle Scholar
  2. 2.
    Celik C, Bilge HS (2017) Content based image retrieval with sparse representations and local feature descriptors: a comparative study. Pattern Recogn 68:1–13CrossRefGoogle Scholar
  3. 3.
    Zhou W, Li H, Tian Q (2017) Recent advance in content-based image retrieval: a literature survey. arXiv:1706.06064
  4. 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. 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 325–332Google Scholar
  6. 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 165–180Google Scholar
  7. 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 27:142–1476CrossRefGoogle Scholar
  8. 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 51–58Google Scholar
  9. 9.
    Attalia E, Siy P (2005) Robust shape similarity retrieval based on contour segmentation polygonal multi resolution and elastic matching. Pattern Recogn 38:2229–2241CrossRefGoogle Scholar
  10. 10.
    Shu X, Wu XJ (2011) A novel contour descriptor for 2D shape matching and its application to image retrieval. Image Vis Comput 29:286–294CrossRefGoogle Scholar
  11. 11.
    Ling H, Okada K (2007) An efficient Earth mover’s distance algorithm for robust histogram comparison. IEEE Trans Pattern Anal Mach Intell 29:840–853CrossRefGoogle Scholar
  12. 12.
    Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24:509–522CrossRefGoogle Scholar
  13. 13.
    Ling H, Jacobs DW (2007) Shape classification using the inner distance. IEEE Trans Pattern Anal Mach Intell 29:286–299CrossRefGoogle Scholar
  14. 14.
    Xu CJ, Liu JZ, Tang X (2009) 2D shape matching by contour flexibility. IEEE Trans Pattern Anal Mach Intell 31:180–186CrossRefGoogle Scholar
  15. 15.
    Xu C, Liu J, Tang X (2009) 2D shape matching by contour flexibility. IEEE Trans Pattern 15 Anal Mach Intell 31(1):180–186CrossRefGoogle Scholar
  16. 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–545Google Scholar
  17. 17.
    Alajlan N, El Rube I, Kamel MS, Freeman G (2007) Shape retrieval using triangle-area representation and dynamic space warping. Pattern Recogn 40:1911–1920CrossRefGoogle Scholar
  18. 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 2289–2296Google Scholar
  19. 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):134–143CrossRefGoogle Scholar
  20. 20.
    McNeill G, Vijayakumar S (2006) Hierarchical procrustes matching for shape retrieval. In: Proceedings of the IEEE conference on CVPR. New York, USA, pp 885–894Google Scholar
  21. 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 357–364Google Scholar
  22. 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):1118–1127MathSciNetCrossRefGoogle Scholar
  23. 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):971–987CrossRefGoogle Scholar
  24. 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):4101–4111MathSciNetCrossRefGoogle Scholar
  25. 25.
    Matsuda Y, Ogawa M, Yano M (2015) Shape retrieval with geometrically characterized contour partitions access. IEEE 3:1161–1178Google Scholar
  26. 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):769–778CrossRefGoogle Scholar
  27. 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 145:43–58CrossRefGoogle Scholar
  28. 28.
    Castellano G, Fanelli AM, Sforza G, Alessandra Torsello M (2016) Shape annotation for intelligent image retrieval. Appl Intell 44(1):179–195CrossRefGoogle Scholar
  29. 29.
    Freitas AM, Torres RS, Paulo AVM (2016) TSB: tensor scale descriptors within circular sectors for fast shape retrieval. Pattern Recogn Lett 83:303–311CrossRefGoogle Scholar
  30. 30.
    Kaothanthong N, Chun J, ratio Takeshi T (2016) Distance interior a new shape signature for 2D shape retrieval. Pattern Recogn Lett 78:14–21CrossRefGoogle Scholar
  31. 31.
    Kundu MK, Chowdhury M, Bulò SR (2015) A graph-based relevance feedback mechanism in content-based image retrieval. Knowl-Based Syst 73:254–264CrossRefGoogle Scholar
  32. 32.
    Chen X (2005) Neural network-based shape retrieval using moment invariants and Zernike moments, Electronic theses and dissertations.2824 university of windsor.
  33. 33.
    Seetharaman K, Sathiamoorthy S (2015) Color image retrieval using statistical model and radial basis function neural network. Egyptian Inform J 15(1):59–68CrossRefGoogle Scholar
  34. 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 002636–002640Google Scholar
  35. 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 723–728Google Scholar
  36. 36.
    Latecki LJ, Lakamper R (2000) Shape similarity measure based on correspondence of visual parts. IEEE Trans Pattern Anal Mach Intell 22(10):1185–1190CrossRefGoogle Scholar
  37. 37.
    Grigorescu C, Petkov N (2003) Distance sets for shape filters and shape recognition. IEEE Trans Image Process 12(10):1274–1286MathSciNetCrossRefGoogle Scholar
  38. 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):742–743CrossRefGoogle Scholar
  39. 39.
    PalazóN-GonzáLez V, Marzal A (2012) On the dynamic time warping of cyclic sequences for shape 7 retrieval. Image Vis Comput 30(12):978–90CrossRefGoogle Scholar
  40. 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 411–424CrossRefGoogle Scholar
  41. 41.
    Bai X, Yang X, Latecki L, Liu W (2010) Learning context-sensitive shape similarity by graph transduction. IEEE Trans 32(5):861–874Google Scholar
  42. 42.
    Bai X, Wang B, Yao C, Liu W, Tu Z (2012) Co-transduction for shape retrieval. IEEE Trans Image Process 21(5):2747–2757MathSciNetCrossRefGoogle Scholar
  43. 43.
    Ion A, Artner NM, Peyré G, Kropatsch WG, Cohen L Matching 2D & 3D Articulated Shapes using EccentricityGoogle Scholar
  44. 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):3935–49MathSciNetCrossRefGoogle Scholar
  45. 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 1–8Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronics EngineeringVITVelloreIndia

Personalised recommendations