Multimedia Tools and Applications

, Volume 75, Issue 3, pp 1667–1699 | Cite as

Point-based medialness for 2D shape description and identification

  • Prashant AparajeyaEmail author
  • Frederic Fol Leymarie


We propose a perception-based medial point description of a natural form (2D: static or in articulated movement) as a framework for a shape representation which can then be efficiently used in biological species identification and matching tasks. Medialness is defined by adapting and refining a definition first proposed in the cognitive science literature when studying the visual attention of human subjects presented with articulated biological 2D forms in movement, such as horses, dogs and humans (walking, running). In particular, special loci of high medialness for the interior of a form in movement, referred to as “hot spots”, prove most attractive to the human perceptual system. We propose an algorithmic process to identify such hot spots. In this article we distinguish exterior from interior shape representation. We further augment hot spots with extremities of medialness ridges identifying significant concavities (from outside) and convexities (from inside). Our representation is strongly footed in results from cognitive psychology, but also inspired by know-how in art and animation, and the algorithmic part is influenced by techniques from more traditional computer vision. A robust shape matching algorithm is designed that finds the most relevant targets from a database of templates by comparing feature points in a scale, rotation and translation invariant way. The performance of our method has been tested on several databases. The robustness of the algorithm is further tested by perturbing the data-set at different levels.


2D shape analysis Dominant points Information retrieval Medialness representation Shape compression Planar articulated movement 



This work was partially funded by the European Union (FP7 – ICT; Grant Agreement #258749; CEEDs project). Thanks to Prof. Stefan Rueger and Prof. Ilona Kovacs for useful discussions.


  1. 1.
    Aparajeya P, Leymarie FF (2014) Point-based medialness for animal and plant identification. In: Vrochidis S, et al. (eds) Proceedings of the 1st International Workshop on Environnmental Multimedia Retrieval, vol. 122., Glasgow, UK, pp 14–21Google Scholar
  2. 2.
    Arnheim R (1974) Art and Visual Perception: A Psychology of the Creative Eye, new version expanded and revised edition of the 1954 original edn, University of California PressGoogle Scholar
  3. 3.
    Bai X, Liu W, Tu Z (2009) Integrating contour and skeleton for shape classification. In: IEEE 12th International Conference on Computer Vision (ICCV) Workshops, pp. 360–367Google Scholar
  4. 4.
    Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (SURF). Comp Vision Image Underst 110(3):346–359CrossRefGoogle Scholar
  5. 5.
    Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 24(4):509–522CrossRefGoogle Scholar
  6. 6.
    Berretti S, Bimbo AD, Pala P (2000) Retrieval by shape similarity with perceptual distance and effective indexing. IEEE Transactions on Multimedia 2 (4):225–239CrossRefGoogle Scholar
  7. 7.
    Biederman I (2001) Recognizing depth-rotated objects: A review of recent research and theory. Spatial Vision 13(2–3):241–253Google Scholar
  8. 8.
    Blum H (1973) Biological shape and visual science. J Theor Biology 38(2):205–287CrossRefGoogle Scholar
  9. 9.
    Bookstein FL (1991) Morphometric Tools for Landmark Data: Geometry and Biology. Cambridge University PressGoogle Scholar
  10. 10.
    Bregler C, Loeb L, Chuang E, Deshpande H (2002) Turning to the masters: Motion capturing cartoons. ACM Trans Graph 21(3):399–407CrossRefGoogle Scholar
  11. 11.
    Caputo B et al (2013) ImageCLEF 2013: The vision, the data and the open challenges. In: Information Access Evaluation. Multilinguality, Multimodality, and Visualization, pp. 250–268. SpringerGoogle Scholar
  12. 12.
    Chen L, Feris R, Turk M (2008) Efficient partial shape matching using Smith-Waterman algorithm. In: Computer Vision and Pattern Recognition Workshops, pp. 1–6Google Scholar
  13. 13.
    Cope J, Corney D, Clark J, Remagnino P, Wilkin P (2012) Plant species identification using digital morphometrics: A review. Expert Syst Appl 39(8):7562–7573CrossRefGoogle Scholar
  14. 14.
    Dougherty ER, Lotufo RA (2003) Hands-On Morphological Image Processing. Tutorial Texts in Optical Engineering, Vol. TT59. SPIE PublicationsGoogle Scholar
  15. 15.
    Everingham M, Gool LV, Williams CKI, Winn J., Zisserman A (2010) The PASCAL visual object classes (VOC) challenge. Int J Comput Vis 88(2):303–338CrossRefGoogle Scholar
  16. 16.
    Gopalan R, Turaga P, Chellappa R (2010) Articulation-invariant representation of non-planar shapes. In: Computer Vision — ECCV 2010, Lecture Notes in Computer Science (LNCS), vol. 6313, pp. 286–299. SpringerCrossRefGoogle Scholar
  17. 17.
    Guay M, Cani MP, Ronfard R (2013) The Line of Action: An intuitive interface, ACM Transactions on Graphics 32(6), Article no 205CrossRefGoogle Scholar
  18. 18.
    Heimann T, Meinzer HP (2009) Statistical shape models for 3D medical image segmentation: A review. Med Image Anal 13(4):543–563CrossRefGoogle Scholar
  19. 19.
    Hu RX, Ja W, Zhao Y, Gui J (2012) Perceptually motivated morphological strategies for shape retrieval. Pattern Recognit 45:3222–3230CrossRefGoogle Scholar
  20. 20.
    Kayaert G, Wagemans J, Vogels R (2011) Encoding of complexity, shape, and curvature by macaque infero-temporal neurons, Frontiers in Systems Neuroscience, vol 5Google Scholar
  21. 21.
    Kelly MF, Levine MD (1995) Annular symmetry operators: A method for locating and describing objects. In: International Conference on Computer Vision (ICCV), pp. 1016–1021Google Scholar
  22. 22.
    Keustermans J, Vandermeulen D, Mollemans W, Schutyser F, Suetens P (2014) Construction of statistical shape models using a probabilistic point-based shape representation. In: Symposium on Statistical Shape Models and Applications, Article 21. Delémont, Switzerland.
  23. 23.
    Kimia BB (2003) On the role of medial geometry in human vision, Journal of Physiology – Paris 97(2) pp. 155–90CrossRefGoogle Scholar
  24. 24.
    Kovács I (2010) Hot spots and dynamic coordination in Gestalt perception. In: Dynamic Coordination in the Brain: From Neurons to Mind, pp. 215–228. MIT PressCrossRefGoogle Scholar
  25. 25.
    Kovács I, Fehér Á, Julesz B (1998) Medial-point description of shape. Vis Res 38(15):2323–2333CrossRefGoogle Scholar
  26. 26.
    Larese MG, Namías R, Craviotto RM, Arango MR, Gallo C, Granitto PM (2014) Automatic classification of legumes using leaf vein image features. Pattern Recog 47:158–168CrossRefGoogle Scholar
  27. 27.
    Latecki LJ, Lakamper R (2000) Shape similarity measure based on correspondence of visual parts. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 22(10):1185–1190CrossRefGoogle Scholar
  28. 28.
    Layton OW, Mingolla E, Yazdanbakhsh A (2014) Neural dynamics of feedforward and feedback processing in figure-ground segregation. Frontiers in Psychology 5(Article 972), 20 pages, Perception Science SeriesGoogle Scholar
  29. 29.
    Leymarie F, Levine MD (1992) Simulating the grassfire transform using an active contour model. IEEE Transactions on Pattern Analysis and Machine Intelligence 14 (1):56–75CrossRefGoogle Scholar
  30. 30.
    Leymarie FF, Aparajeya P, MacGillivray C (2014) Point-based medialness for movement computing. In: Proceedings of the 1st ACM International Workshop on Movement and Computing (MOCO), pp. 31–36Google Scholar
  31. 31.
    Leyton M (1992) Symmetry, Causality, Mind. MIT PressGoogle Scholar
  32. 32.
    Ling H, Jacobs DW (2007) Shape classification using the inner-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(2):286–299CrossRefGoogle Scholar
  33. 33.
    Liu Z, An J, Meng F (2011) A robust point matching algorithm for image registration. In: Fourth International Conference on Machine Vision (ICMV), vol. SPIE 8350Google Scholar
  34. 34.
    Loomis A (1951) Successful Drawing, Viking BooksGoogle Scholar
  35. 35.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  36. 36.
    Mingqiang Y, Kidiyo K, Joseph R (2008) A survey of shape feature extraction techniques. In: Yin PY (ed) Pattern Recognition Techniques, Technology and Applications, chap. 3, pp. 43–90. InTechGoogle Scholar
  37. 37.
    Mouine S, Yahiaoui I, Verroust-Blondet A (2012) Advanced shape context for plant species identification using leaf image retrieval. In: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval (ICMR), Article 49. 8 pagesGoogle Scholar
  38. 38.
    Nanni L, Lumini A, Brahnam S (2014) Ensemble of shape descriptors for shape retrieval and classification. International Journal of Advanced Intelligence Paradigms 6 (2):136–156CrossRefGoogle Scholar
  39. 39.
    Park J, Hwang E, Nam Y (2008) Utilizing venation features for efficient leaf image retrieval. J Syst Softw 81(1):71–82CrossRefGoogle Scholar
  40. 40.
    Pizer SM, Siddiqi K, Székely G, Damon JN, Zucker SW (2003) Multiscale medial loci and their properties. Int J Comput Vis 55(2/3):155–179CrossRefGoogle Scholar
  41. 41.
    Pizer SM, et al. (2003) Deformable M-reps for 3D medical image segmentation. Int J Comput Vis 55(2/3):85–106CrossRefGoogle Scholar
  42. 42.
    Premachandran V, Kakarala R (2013) Perceptually motivated shape context which uses shape interiors. Pattern Recognit 46:2092–2102CrossRefGoogle Scholar
  43. 43.
    Richards W, Hoffman DD (1985) Codon constraints on closed 2D shapes. Computer Vision. Graphics, and Image Processing (CVGIP) 31(3):265–281CrossRefGoogle Scholar
  44. 44.
    Rijsbergen CJV (1979) Information Retrieval, 2nd edn, Butterworth-HeinemannGoogle Scholar
  45. 45.
    Roman-Rangel E, Gayol CP, Odobez JM (2011) Searching the past: An improved shape descriptor to retrieve Maya hieroglyphs. In: ACM Multimedia. Scottsdale, Arizona, USAGoogle Scholar
  46. 46.
    Sebastian TB, Klein PN, Kimia BB (2004) Recognition of shapes by editing their shock graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5):550–571CrossRefGoogle Scholar
  47. 47.
    Serra J (ed) (1988) Image Analysis and Mathematical Morphology, vol. 2. Academic PressGoogle Scholar
  48. 48.
    Shen W, Wang X, Yao C, Bai X (2014) Shape recognition by combining contour and skeleton into a mid-level representation. In: Li S., Liu C., Wang Y. (eds) Pattern Recognition, vol 483. Springer, pp 391–400Google Scholar
  49. 49.
    Simmons S, Winer MSA (1977) Drawing: The Creative Process, Simon and Schuster (Prentice-Hall)Google Scholar
  50. 50.
    Srestasathiern P, Yilmaz A (2011) Planar shape representation and matching under projective transformation. Comp Vision Image Underst (CVIU) 115(11):1525–1535CrossRefGoogle Scholar
  51. 51.
    Tang J, Shao L, Jones S (2014) Point pattern matching based on line graph spectral context and descriptor embedding. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 17–22Google Scholar
  52. 52.
    van Tonder GJ, Ejima Y (2003) Flexible computation of shape symmetries within the maximal disk paradigm. IEEE Transactions on Systems, Man, and Cybernetics (SMC), Part B: Cybernetics, 33(3), pp. 535–540Google Scholar
  53. 53.
    Vincent L. (1993) Morphological grayscale reconstruction in image analysis. IEEE Trans Image Process 2(2):176–201CrossRefGoogle Scholar
  54. 54.
    Wamelen PBV, Li Z, Iyengar SS (2004) A fast expected time algorithm for the 2-D point pattern matching problem. Pattern Recognit 37(8):1699–711CrossRefGoogle Scholar
  55. 55.
    Wang X, Feng B, Bai X, Liu W, Latecki LJ (2014) Bag of contour fragments for robust shape classification. Pattern Recognit 47(6):2116–2125CrossRefGoogle Scholar
  56. 56.
    Xie J, Heng PA, Shah M (2008) Shape matching and modeling using skeletal context. Pattern Recognit 41(5):1756–1767CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Deparment of Computing, GoldsmithsUniversity of LondonLondonUK

Personalised recommendations