Multimedia Tools and Applications

, Volume 76, Issue 6, pp 7823–7848 | Cite as

2D and 3D shape retrieval using skeleton filling rate

  • Yahya Sirin
  • M. Fatih DemirciEmail author


As an increasing number of digital images are generated, a demand for an efficient and effective image retrieval mechanisms grows. In this work, we present a new skeleton-based algorithm for 2D and 3D shape retrieval. The algorithm starts by drawing circles (spheres for 3D) of increasing radius around skeletons. Since each skeleton corresponds to the center of a maximally inscribed circle (sphere), this process results in circles (spheres) that are partially inside the shape. Computing the ratio between pixels that lie within the shape and the total number of pixels allows us to distinguish shapes with similar skeletons. Experimental evaluation of the proposed approach including a comprehensive comparison with the previous techniques demonstrates both effectiveness and robustness of our algorithm for shape retrieval using several 2D and 3D datasets.


Shape recognition Shape retrieval Earth mover’s distance 2D and 3D skeleton 


  1. 1.
    Akgul CB, Sankur B, Yemez Y, Schmitt F (2009) 3d model retrieval using probability density-based shape descriptors. IEEE Trans Pattern Anal Mach Intell 31 (6):1117–1133zbMATHCrossRefGoogle Scholar
  2. 2.
    Akimaliev M, Demirci MF (2015) Improving skeletal shape abstraction using multiple optimal solutions. Pattern Recogn 48(11):3504–3515CrossRefGoogle Scholar
  3. 3.
    Andaló FA, Miranda PAV, Torres RDS, Falcão AX (2010) Shape feature extraction and description based on tensor scale. Pattern Recogn 43(1):26–36zbMATHCrossRefGoogle Scholar
  4. 4.
    Ankerst M, Kastenmüller G, Kriegel H-P, Seidl T (1999) 3d shape histograms for similarity search and classification in spatial databases. In: Advances in spatial databases. Springer, pp 207–226Google Scholar
  5. 5.
    Arbter K, Snyder WE, Burhardt H, Hirzinger G (1990) Application of affine-invariant fourier descriptors to recognition of 3-d objects. IEEE Trans Pattern Anal Mach Intell 12(7):640–647CrossRefGoogle Scholar
  6. 6.
    Axenopoulos A, Litos G, Daras P (2011) 3d model retrieval using accurate pose estimation and view-based similarity. In: Proceedings of the 1st ACM international conference on multimedia retrieval. ACM, p 41Google Scholar
  7. 7.
    Belongie Serge, Malik Jitendra, Puzicha Jan (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24 (4):509–522CrossRefGoogle Scholar
  8. 8.
    Ben-Chen M, Gotsman C (2008) Characterizing shape using conformal factors. In: 3DOR, pp 1–8Google Scholar
  9. 9.
    Bronstein AM, Bronstein MM, Bruckstein AM, Kimmel R (2008) Analysis of two-dimensional non-rigid shapes. Int J Comput Vis 78(1):67–88CrossRefGoogle Scholar
  10. 10.
    Bustos B, Schreck T, Walter M, Barrios JM, Schaefer M, Keim D (2012) Improving 3d similarity search by enhancing and combining 3d descriptors. Multimed Tools Appl 58(1):81–108CrossRefGoogle Scholar
  11. 11.
    Chang X, Yang Y, Hauptmann AG, Xing EP, Yu Y-L (2015) Semantic concept discovery for large-scale zero-shot event detection. In: Proceedings of IJCAIGoogle Scholar
  12. 12.
    Chang X, Yang Y, Xing E, Yu Y (2015) Complex event detection using semantic saliency and nearly-isotonic svm. In: Proceedings of the 32nd international conference on machine learning (ICML-15), pp 1348–1357Google Scholar
  13. 13.
    Chang X, Yu Y-L, Yang Y, Hauptmann AG (2015) Searching persuasively: Joint event detection and evidence recounting with limited supervision. In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference. ACM, pp 581–590Google Scholar
  14. 14.
    Chellappa R, Bagdazian R (1984) Fourier coding of image boundaries. IEEE Trans Pattern Anal Mach Intell 6(1):102–105CrossRefGoogle Scholar
  15. 15.
    Chen D-Y, Tian X-P, Shen Y-T, Ouhyoung M (2003) On visual similarity based 3d model retrieval. In: Computer graphics forum, vol 22. Wiley online library, pp 223–232Google Scholar
  16. 16.
    Cohen S, Guibas L (1999) The earth mover’s distance under transformation sets. In: Computer vision, 1999. The proceedings of the seventh IEEE international conference on, vol 2. IEEE, pp 1076–1083Google Scholar
  17. 17.
    Cornea ND, Demirci MF, Silver D, Shokoufandeh A, Dickinson SJ, Kantor PB (2005) 3d object retrieval using many-to-many matching of curve skeletons. In: Shape Modeling and Applications, 2005 International Conference. IEEE, pp 366–371Google Scholar
  18. 18.
    Daliri MR, Torre V (2008) Robust symbolic representation for shape recognition and retrieval. Pattern Recogn 41(5):1782–1798zbMATHCrossRefGoogle Scholar
  19. 19.
    Demirci MF, Shokoufandeh A, Keselman Y, Dickinson S, Bretzner L (2003) Many-to-many matching of scale-space feature hierarchies using metric embedding. In: Griffin LD, Lillholm M (eds) Scale Space Methods in Computer Vision, volume 2695 of Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp 17–32Google Scholar
  20. 20.
    Demirci MF, Platel B, Shokoufandeh A, Florack L, Dickinson S (2009) The representation and matching of images using top points. J Math Imaging Vis 35 (2):103–116MathSciNetCrossRefGoogle Scholar
  21. 21.
    Demirci MF, Osmanlioglu Y, Shokoufandeh A, Dickinson S (2011) Efficient many-to-many feature matching under the 1 norm. Comput Vis Image Underst 115(7):976–983CrossRefGoogle Scholar
  22. 22.
    Donoser M, Bischof H (2013) Diffusion processes for retrieval revisited. In: 2013 IEEE conference on Computer vision and pattern recognition (CVPR). IEEE, pp 1320–1327Google Scholar
  23. 23.
    Eberly D (1994) A differential geometric approach to anisotropic diffusion. In: Bart M, Romeny TH (eds) Geometry-Driven Diffusion in Computer Vision, volume 1 of Computational Imaging and Vision. Springer, Netherlands, pp 371–392Google Scholar
  24. 24.
    Ebrahim Y, Ahmed M, Abdelsalam W, Chau S-C (2009) Shape representation and description using the hilbert curve. Pattern Recogn Lett 30(4):348–358CrossRefGoogle Scholar
  25. 25.
    Eitz M, Richter R, Boubekeur T, Hildebrand K, Alexa M (2012) Sketch-based shape retrieval. ACM Trans Graph 31(4):31Google Scholar
  26. 26.
    Frejlichowski D (2011) A three-dimensional shape description algorithm based on polar-fourier transform for 3d model retrieval. In: Heyden A, Kahl F (eds) Image Analysis, volume 6688 of Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp 457–466Google Scholar
  27. 27.
    Funkhouser T, Min P, Kazhdan M, Chen J, Halderman A, Dobkin D, Jacobs D (2003) A search engine for 3d models. ACM Trans Graph (TOG) 22 (1):83–105CrossRefGoogle Scholar
  28. 28.
    Furuya T, Ohbuchi R (2009) Dense sampling and fast encoding for 3d model retrieval using bag-of-visual features. In: Proceedings of the ACM international conference on image and video retrieval. ACM, p 26Google Scholar
  29. 29.
    Gal R, Shamir A, Cohen-Or D (2007) Pose-oblivious shape signature. IEEE Trans Vis Comput Graph 13(2):261–271CrossRefGoogle Scholar
  30. 30.
    Gopalan R, Turaga P, Chellappa R (2010) Articulation-invariant representation of non-planar shapes. In: Computer vision–ECCV 2010. Springer, pp 286–299Google Scholar
  31. 31.
    Granlund GH (1972) Fourier preprocessing for hand print character recognition. IEEE Trans Comput 21(2):195–201MathSciNetzbMATHCrossRefGoogle Scholar
  32. 32.
    Guocheng A, Fengjun Z, Hong’an W, Guozhong D (2010) Shape filling rate for silhouette representation and recognition. In: 2010 20th international conference on Pattern recognition (ICPR). IAPR, pp 507–510Google Scholar
  33. 33.
    Hilaga M, Shinagawa Y, Kohmura T, Kunii TL (2001) Topology matching for fully automatic similarity estimation of 3d shapes. In: Proceedings of the 28th annual conference on computer graphics and interactive techniques. ACM, pp 203–212Google Scholar
  34. 34.
    Horn BKP (1984) Extended gaussian images. IEEE Proc 72(12):1671–1686CrossRefGoogle Scholar
  35. 35.
    Hu R-X, Jia W, Zhao Ya, Gui J (2012) Perceptually motivated morphological strategies for shape retrieval. Pattern Recogn 45(9):3222–3230CrossRefGoogle Scholar
  36. 36.
    Iyer N, Jayanti S, Lou K, Kalyanaraman Y, Ramani K (2005) Three-dimensional shape searching: state-of-the-art review and future trends. Comput Aided Des 37(5):509–530CrossRefGoogle Scholar
  37. 37.
    Iyer N, Kalyanaraman Y, Lou K, Jayanti S, Ramani K (2003) A reconfigurable 3d engineering shape search system: Part i-shape representation. In: ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, pp 89–98Google Scholar
  38. 38.
    Kang SB, Ikeuchi K (1991) Determining 3-d object pose using the complex extended gaussian image. In: IEEE computer society conference on Computer vision and pattern recognition, 1991. Proceedings CVPR’91. IEEE, pp 580–585Google Scholar
  39. 39.
    Kauppinen H, Seppänen T, Pietikäinen M (1995) An experimental comparison of autoregressive and fourier-based descriptors in 2d shape classification. IEEE Trans Pattern Anal Mach Intell 17(2):201–207CrossRefGoogle Scholar
  40. 40.
    Kawamura S, Usui K, Furuya T, Ohbuchi Rx (2012) Local goemetrical feature with spatial context for shape-based 3d model retrieval. In: 3DOR, pp 55–58Google Scholar
  41. 41.
    Kazhdan M, Funkhouser T, Rusinkiewicz S (2003) Rotation invariant spherical harmonic representation of 3 d shape descriptors. In: Symposium on geometry processing, vol 6Google Scholar
  42. 42.
    Kim H-K, Kim J-D (2000) Region-based shape descriptor invariant to rotation, scale and translation. Sig Proc Image Comm 16(1-2):87–93CrossRefGoogle Scholar
  43. 43.
    Kuang Z, Li Z, Jiang X, Liu Y, Li H (2015) Retrieval of non-rigid 3d shapes from multiple aspects. Comput Aided Des 58:13–23CrossRefGoogle Scholar
  44. 44.
    Laiche N, Larabi S, Ladraa F, Khadraoui Ax (2014) Curve norMalization for shape retrieval. Signal Process Image Commun 29(4):556–571CrossRefGoogle Scholar
  45. 45.
    Leng B, Xiong Z (2011) Modelseek: an effective 3d model retrieval system. Multimed Tools Appl 51(3):935–962CrossRefGoogle Scholar
  46. 46.
    Li B, Johan H (2013) 3d model retrieval using hybrid features and class information. Multimed Tools Appl 62(3):821–846CrossRefGoogle Scholar
  47. 47.
    Li P, Wang Q, Zhang L (2013) A novel earth mover’s distance methodology for image matching with gaussian mixture models ICCVGoogle Scholar
  48. 48.
    Li S-S, Huang Y-D, Yang J-W (2013) Affine invariant ring fourier descriptors. In: International conference on wavelet analysis and pattern recognition, pp 62–66Google Scholar
  49. 49.
    Lian Z, Godil A, Bustos B, Daoudi M, Hermans J, Kawamura S, Kurita Y, Lavoué G, Nguyen HV, Ohbuchi R et al (2013) A comparison of methods for non-rigid 3d shape retrieval. Pattern Recogn 46(1):449–461CrossRefGoogle Scholar
  50. 50.
    Lian Z, Rosin PL, Sun X (2010) Rectilinearity of 3d meshes. Int J Comput Vis 89(2-3):130–151CrossRefGoogle Scholar
  51. 51.
    Lin CC, Chellappa R (1987) Classification of partial 2d shapes using fourier descriptors. IEEE Trans Pattern Anal Mach Intell 9(5):686–690CrossRefGoogle Scholar
  52. 52.
    Ling H, Jacobs DW (2005) Using the inner-distance for classification of articulated shapes. In: IEEE computer society conference on Computer vision and pattern recognition, 2005. CVPR 2005, vol 2. IEEE, pp 719–726Google Scholar
  53. 53.
    Ling H, Jacobs DW (2007) Shape classification using the inner-distance. IEEE Trans Pattern Anal Mach Intell 29(2):286–299CrossRefGoogle Scholar
  54. 54.
    Liu T-L, Geiger D (1999) Approximate tree matching and shape similarity. In: The proceedings of the seventh IEEE international conference on Computer vision, 1999, vol 1. IEEE, pp 456–462Google Scholar
  55. 55.
    Lou K, Jayanti S, Iyer N, Kalyanaraman Y, Prabhakar S, Ramani K (2003) A reconfigurable 3d engineering shape search system: Part ii-database indexing, retrieval, and clustering. In: ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, pp 169–178Google Scholar
  56. 56.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  57. 57.
    Mémoli F, Sapiro G (2005) A theoretical and computational framework for isometry invariant recognition of point cloud data. Found Comput Math 5(3):313–347MathSciNetzbMATHCrossRefGoogle Scholar
  58. 58.
    Nanni L, Brahnam S, Lumini Ax (2012) Local phase quantization descriptor for improving shape retrieval/classification. Pattern Recogn Lett 33(16):2254–2260CrossRefGoogle Scholar
  59. 59.
    Novotni M, Klein R (2004) Shape retrieval using 3d zernike descriptors. Comput Aided Des 36(11):1047–1062CrossRefGoogle Scholar
  60. 60.
    Ohishi Y, Ohbuchi R (2013) Densely sampled local visual features on 3d mesh for retrieval. In: 2013 14th international workshop on Image analysis for multimedia interactive services (WIAMIS). IEEE, pp 1–4Google Scholar
  61. 61.
    Ohkita Y, Ohishi Y, Furuya T, Ohbuchi R (2012) Non-rigid 3d model retrieval using set of local statistical features. In: 2012 IEEE international conference on Multimedia and expo workshops (ICMEW). IEEE, pp 593–598Google Scholar
  62. 62.
    Osada R, Funkhouser T, Chazelle B, Dobkin D (2001) Matching 3d models with shape distributions. In: SMI 2001 international conference on Shape modeling and applications. IEEE, pp 154–166Google Scholar
  63. 63.
    Osada R, Funkhouser T, Chazelle B, Dobkin D (2002) Shape distributions. ACM Trans Graph (TOG) 21(4):807–832MathSciNetzbMATHCrossRefGoogle Scholar
  64. 64.
    Papadakis P, Pratikakis I, Perantonis S, Theoharis T (2007) Efficient 3d shape matching and retrieval using a concrete radialized spherical projection representation. Pattern Recogn 40(9):2437–2452zbMATHCrossRefGoogle Scholar
  65. 65.
    Papadakis P, Pratikakis I, Theoharis T, Passalis G, Perantonis S (2008) 3d object retrieval using an efficient and compact hybrid shape descriptor. In: Eurographics workshop on 3d object retrievalGoogle Scholar
  66. 66.
    Papadakis P, Pratikakis I, Theoharis T, Perantonis S (2010) Panorama: A 3d shape descriptor based on panoramic views for unsupervised 3d object retrieval. Int J Comput Vis 89(2-3):177–192CrossRefGoogle Scholar
  67. 67.
    Pedrosa GV, Batista MA, Barcelos CAZ (2013) Image feature descriptor based on shape salience points. Neurocomputing 120:156–163CrossRefGoogle Scholar
  68. 68.
    Pele O, Werman M (2009) Fast and robust earth mover’s distances. In: ICCVGoogle Scholar
  69. 69.
    Rauber TW, Steiger-Garcao AS (1992) Shape description by unl fourier features-an application to handwritten character recognition. In: 11Th IAPR international conference on pattern recognition, pp 466–469Google Scholar
  70. 70.
    Rubner Y, Tomasi C, Guibas LJ (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vis 40(2):99–121zbMATHCrossRefGoogle Scholar
  71. 71.
    Ruggeri MR, Patanè G, Spagnuolo M, Saupe D (2010) Spectral-driven isometry-invariant matching of 3d shapes. Int J Comput Vis 89(2-3):248–265CrossRefGoogle Scholar
  72. 72.
    Schreck T, Scherer M, Walter M, Bustos B, Yoon SM, Kuijper A (2012) Graph-based combinations of fragment descriptors for improved 3d object retrieval. In: Proceedings of the 3rd multimedia systems conference. ACM, pp 23–28Google Scholar
  73. 73.
    Sebastian TB, Klein PN, Kimia BB (2004) Recognition of shapes by editing their shock graphs. IEEE Trans Pattern Anal Mach Intell 26(5):550–571CrossRefGoogle Scholar
  74. 74.
    Sharvit D, Chan J, Tek H, Kimia B (1998) Symmetry-based indexing of image databases. In: 1998. Proceedings. IEEE workshop on Content-based access of image and video libraries. IEEE , pp 56–62Google Scholar
  75. 75.
    Shekar BH, Pilar B (2014) Shape representation and classification through pattern spectrum and local binary pattern–a decision level fusion approach. In: 2014 fifth international conference on Signal and image processing (ICSIP). IEEE, pp 218–224Google Scholar
  76. 76.
    Shekar BH, Pilar B, Kittler J (2015) An unification of inner distance shape context and local binary pattern for shape representation and classification. In: Proceedings of the 2nd international conference on perception and machine intelligence. ACM, pp 46–55Google Scholar
  77. 77.
    Shen W, Bai X, Hu R, Wang H, Latecki LJ (2011) Skeleton growing and pruning with bending potential ratio. Pattern Recogn 44(2):196–209CrossRefGoogle Scholar
  78. 78.
    Shen Y-T, Chen D-Y, Tian X-P, Ouhyoung M (2003) 3D model search engine based on lightfield descriptors. In: EurographicsGoogle Scholar
  79. 79.
    Shih J-L, Chen H-Y (2009) A 3d model retrieval approach using the interior and exterior 3d shape information. Multimed Tools Appl 43(1):45–62MathSciNetCrossRefGoogle Scholar
  80. 80.
    Shih J-L, Lee C-H, Wang JTa (2007) A new 3d model retrieval approach based on the elevation descriptor. Pattern Recogn 40(1):283–295zbMATHCrossRefGoogle Scholar
  81. 81.
    Shilane P, Min P, Kazhdan M, Funkhouser T (2004) The princeton shape benchmark. In: Shape modeling applications, 2004. proceedings. IEEE, pp 167–178Google Scholar
  82. 82.
    Shu X, Wu X-J (2011) A novel contour descriptor for 2d shape matching and its application to image retrieval. Image Vis Comput 29(4):286–294CrossRefGoogle Scholar
  83. 83.
    Siddiqi K, Bouix S, Tannenbaum A, Zucker SW (2002) Hamilton-jacobi skeletons. Int J Comput Vis 48(3):215–231zbMATHCrossRefGoogle Scholar
  84. 84.
    Siddiqi K, Zhang J, Macrini D, Shokoufandeh A, Bouix S, Dickinson S (2008) Retrieving articulated 3-d models using medial surfaces. Mach Vis Appl 19 (4):261–275zbMATHCrossRefGoogle Scholar
  85. 85.
    Sipiran I, Bustos B, Schreck T (2013) Data-aware 3d partitioning for generic shape retrieval. Comput Graph 37(5):460–472CrossRefGoogle Scholar
  86. 86.
    Sirin Y, Demirci MF (2014) Skeleton filling rate for shape recognition. In: 2014 22nd international conference on Pattern recognition (ICPR). IAPR, pp 4005–4009Google Scholar
  87. 87.
    Söderkvist O (2001) Computer vision classification of leaves from swedish treesGoogle Scholar
  88. 88.
    Sun J, Ovsjanikov M, Guibas L (2009) A concise and provably informative multi-scale signature based on heat diffusion. In: Computer graphics forum, vol 28. Wiley online library, pp 1383–1392Google Scholar
  89. 89.
    Sundar H, Silver D, Gagvani N, Dickinson S (2003) Skeleton based shape matching and retrieval. In: Shape modeling international, 2003. IEEE, pp 130–139Google Scholar
  90. 90.
    Tam GKL, Lau RWH (2007) Deformable model retrieval based on topological and geometric signatures. IEEE Trans Vis Comput Graph 13(3):470–482CrossRefGoogle Scholar
  91. 91.
    Tangelder JWH, Veltkamp RC (2008) A survey of content based 3d shape retrieval methods. Multimed Tools Appl 39(3):441–471CrossRefGoogle Scholar
  92. 92.
    Van der Zwan M, Meiburg Y, Telea A (2013) A dense medial descriptor for image analysis. In: VISAPP (1), pp 285–293Google Scholar
  93. 93.
    Van Otterloo PJ (1991) A Contour-oriented Approach to Shape Analysis. Prentice Hall International (UK) Ltd., Hertfordshire, UKzbMATHGoogle Scholar
  94. 94.
    Vranic DV (2005) Desire: a composite 3d-shape descriptor. In: IEEE international conference on Multimedia and expo, 2005. ICME 2005. IEEE, pp 4–ppGoogle Scholar
  95. 95.
    Vranić DV, Saupe D (2004) 3d model retrieval. In: Proc SCCG 2000, pp 3–6Google Scholar
  96. 96.
    Wang Fan, Guibas LJ (2012) Supervised earth mover’s distance learning and its computer vision applications. In: Computer vision–ECCV 2012. Springer, pp 442–455Google Scholar
  97. 97.
    Wang J, Bai X, You X, Liu W, Latecki LJ (2012) Shape matching and classification using height functions. Pattern Recogn Lett 33(2):134–143CrossRefGoogle Scholar
  98. 98.
    Wu J, Rehg JM (2008) Where am i: Place instance and category recognition using spatial pact. In: 2008. CVPR 2008. IEEE conference on Computer vision and pattern recognition. IEEE, pp 1–8Google Scholar
  99. 99.
    Xie J, Heng P-A, Shah M (2008) Shape matching and modeling using skeletal context. Pattern Recogn 41(5):1756–1767zbMATHCrossRefGoogle Scholar
  100. 100.
    Xu J, Zhang Z, Tung AK, Yu G (2012) Efficient and effective similarity search over probabilistic data based on earth mover’s distance. VLDB J Int J Very Large Data Bases 21(4):535–559CrossRefGoogle Scholar
  101. 101.
    Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recogn 37(1):1–19CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer EngineeringTOBB University of Economics and TechnologyAnkaraTurkey

Personalised recommendations