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Evaluating contour segment descriptors

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Abstract

Contour segment (CS) is the fundamental element of partial boundaries or edges in shapes and images. So far, CS has been widely used in many applications, including object detection/matching and open curve matching. To increase the matching accuracy and efficiency, a variety of CS descriptors have been proposed. A CS descriptor is formed by a chain of boundary or edge points and is able to encode the geometric configuration of a CS. Because many different CS descriptors exist, a structured overview and quantitative evaluation are required in the context of CS matching. This paper assesses 27 CS descriptors in a structured way. Firstly, the analytical invariance properties of CS descriptors are explored with respect to scaling, rotation and transformation. Secondly, their distinctiveness is evaluated experimentally on three datasets. Lastly, their computation complexity is studied. Based on results, we find that both CS lengths and matching algorithms affect the CS matching performance while matching algorithms have higher affection. The results also reveal that, with different combinations of CS descriptors and matching algorithms, several requirements in terms of matching speed and accuracy can be fulfilled. Furthermore, a proper combination of CS descriptors can improve the matching accuracy over the individuals.

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References

  1. Al-Naymat, G., Chawla, S., Taheri, J.: Sparsedtw: a novel approach to speed up dynamic time warping. In: Australasian Data Mining Conference, pp. 117–127 (2009)

  2. Alajlan, N., Rube, I.E., Kamel, M.S., Freeman, G.: Shape retrieval using triangle-area representation and dynamic space warping. Pattern Recogn. 40(7), 1911–1920 (2007)

    Article  MATH  Google Scholar 

  3. Andrew, A.: Another efficient algorithm for convex hulls in two dimensions. Inf. Process. Lett. 9(5), 216–219 (1979)

    Article  MATH  Google Scholar 

  4. Arbter, K., Snyder, W.E., Burhardt, H., Hirzinger, G.: Application of affine-invariant fourier descriptors to recognition of 3-d objects. IEEE Trans. PAMI 12(7), 640–647 (1990)

    Article  Google Scholar 

  5. Bai, X., Latecki, L., Liu, W.: Skeleton pruning by contour partitioning with discrete curve evolution. IEEE Trans. PAMI 29(3), 449–462 (2007)

    Article  Google Scholar 

  6. Baust, M., Demaret, L., Storath, M., Navab, N., Weinmann, A.: Total variation regularization of shape signals. In: IEEE CVPR, pp. 2075–2083 (2015)

  7. Bellman, R.: The theory of dynamic programming. Bull. Am. Math. Soc. 60(6), 503–516 (1954)

    Article  MathSciNet  MATH  Google Scholar 

  8. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. PAMI 24(4), 509–522 (2002)

    Article  Google Scholar 

  9. Bertasius, G., Shi, J., Torresani, L.: Deepedge: a multi-scale bifurcated deep network for top-down contour detection. In: IEEE CVPR, pp. 4380–4389 (2015)

  10. Bhattacharyya, A.: On a measure of divergence between two multinomial populations. Indian J. Stat. 7(4), 401–406 (1946)

    MathSciNet  MATH  Google Scholar 

  11. Bronstein, A.M., Bronstein, M.M., Bruckstein, A.M., Kimmel, R.: Partial similarity of objects, or how to compare a centaur to a horse. IJCV 84(2), 163–183 (2009)

    Article  Google Scholar 

  12. Burkard, R.E., Dell’Amico, M., Martello, S.: Assignment Problems, Revised Reprint, pp. 93–99. SIAM (2009)

  13. Chellappa, R., Bagdazian, R.: Fourier coding of image boundaries. IEEE Trans. PAMI 6(1), 102–105 (1984)

    Article  Google Scholar 

  14. Chen, L., Feris, R., Turk, M.: Efficient partial shape matching using smith-waterman algorithm. In: CVPR, pp. 1–6 (2008)

  15. Cootes, T.F., Cooper, D., Taylor, C., Graham, J.: Trainable method of parametric shape description. Image Vis. Comput. 10(5), 289–294 (1992)

    Article  Google Scholar 

  16. Daliri, M.R., Torre, V.: Robust symbolic representation for shape recognition and retrieval. Pattern Recogn. 41(5), 1782–1798 (2008)

    Article  MATH  Google Scholar 

  17. Daliri, M.R., Torre, V.: Classification of silhouettes using contour fragments. Comput. Vis. Image Underst. 113(9), 1017–1025 (2009)

    Article  Google Scholar 

  18. Daliri, M.R., Torre, V.: Shape recognition based on kernel-edit distance. Comput. Vis. Image Underst. 114(10), 1097–1103 (2010)

    Article  Google Scholar 

  19. de Junior, Mesquita Sa J.J., Backes, A.R.: Shape classification using line segment statistics. Inf. Sci. 305, 349–356 (2015)

    Article  Google Scholar 

  20. Donoser, M., Riemenschneider, H., Bischof, H.: Efficient partial shape matching of outer contours. In: ACCV, pp. 281–292 (2010)

  21. Eitz, M., Richter, R., Boubekeur, T., Hildebrand, K., Alexa, M.: Sketch-based shape retrieval. ACM Graph. 31(4), 1–10 (2012)

    Google Scholar 

  22. Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  23. Ferrari, V., Tuytelaars, T., Gool, L.V.: Object detection by contour segment networks. In: ECCV, pp. 14–28 (2006)

  24. Hariharan, B., Arbelaez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Mortensen, E., Fidler, S. (eds.) IEEE CVPR, pp. 447–456 (2015)

  25. Harris, J.W., Stocker, H.: Segment of a circle. In: Stocker, H. (ed.) Handbook of Mathematics and Computational Science, pp. 92–93. Springer, New York, USA (1998)

    Chapter  Google Scholar 

  26. Homer, S., Selman, A.: Introduction to complexity theory. In: Gries, D., Schneider, FB. (eds.) Computability and Complexity Theory. Texts in Computer Science, pp. 75–80. Springer, New York, USA (2011)

  27. Karczmarek, P., Kiersztyn, A., Pedrycz, W., Rutka, P.: Chain code-based local descriptor for face recognition. In: CORES, pp. 10–20 (2015)

  28. Kauppinen, H., Seppanen, T., Pietikainen, M.: An experimental comparison of autoregressive and fourier-based descriptors in 2d shape classification. IEEE Trans. PAMI 17(2), 201–207 (1995)

    Article  Google Scholar 

  29. Kontschieder, P., Riemenschneider, H., Donoser, M., Bischof, H.: Discriminative learning of contour fragments for object detection. In: BMVC, pp. 1–12 (2011)

  30. Krzyzak, A., Leung, S., Suen, C.: Reconstruction of two-dimensional patterns from fourier descriptors. Mach. Vis. Appl. 2(3), 123–140 (1989)

    Article  Google Scholar 

  31. Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logist. Q. 2, 83–97 (1955)

    Article  MathSciNet  MATH  Google Scholar 

  32. Kurtzberg, J.M.: On approximation methods for the assignment problem. J. ACM 9(4), 419–439 (1962)

    Article  MathSciNet  MATH  Google Scholar 

  33. Latecki, L.J., Lakamper, R., Eckhardt, T.: Shape descriptors for non-rigid shapes with a single closed contour. In: Werner, B. (ed.) IEEE CVPR, pp. 424–429. IEEE Computer Society, Los Alamitos, CA, USA (2000)

    Google Scholar 

  34. Liu, L., Shell, D.: Assessing optimal assignment under uncertainty: an interval-based algorithm. In: Ayanian, N., Kuindersma, S. (eds.) Robotics: Science and Systems. The MIT Press, Cambridge, MA USA (2010)

    Google Scholar 

  35. Liu, Y., Gall, J., Stoll, C., Dai, Q., Seidel, H.P., Theobalt, C.: Markerless motion capture of multiple characters using multiview image segmentation. IEEE Trans. PAMI 35(11), 2720–2735 (2013)

    Article  Google Scholar 

  36. Lu, C., Latecki, L., Adluru, N., Yang, X., Ling, H.: Shape guided contour grouping with particle filters. In: IEEE ICCV, pp. 2288–2295 (2009)

  37. Ma, T., Latecki, L.: From partial shape matching through local deformation to robust global shape similarity for object detection. In: IEEE CVPR, pp. 1441–1448 (2011)

  38. Ma, T., Latecki, L.J.: From partial shape matching through local deformation to robust global shape similarity for object detection. In: IEEE CVPR, pp. 1441–1448 (2011)

  39. Maheshwari, A., Sack, J.R., Shahbaz, K., Zarrabi-Zadeh, H.: Improved algorithms for partial curve matching. Algorithmica 69(3), 641–657 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  40. Ohm, J.R., Bunjamin, F., Liebsch, W., Makai, B., Mller, K., Smolic, A., Zier, D.: A set of visual feature descriptors and their combination in a low-level description scheme. Sig. Process. Image Commun. 16(12), 157–179 (2000)

    Article  Google Scholar 

  41. Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285–296), 23–27 (1975)

    Google Scholar 

  42. Payet, N., Todorovic, S.: From a set of shapes to object discovery. In: ECCV, pp. 57–70 (2010)

  43. Peura, M., Iivarinen, J.: Efficiency of simple shape descriptors. In: Aspects of visual form, pp. 443–451 (1997)

  44. Plackett, R.L.: Karl Pearson and the chi-squared test. Int. Stat. Rev. 51(1), 5972 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  45. Riemenschneider, H., Donoser, M., Bischof, H.: Using partial edge contour matches for efficient object category localization. In: ECCV, pp. 29–42 (2010)

  46. Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. IJCV 40(2), 99–121 (2000)

    Article  MATH  Google Scholar 

  47. Salvador, S., Chan, P.: Fastdtw: toward accurate dynamic time warping in linear time and space. In: KDD, pp. 70–80 (2004)

  48. Sellers, P.H.: The theory and computation of evolutionary distances: pattern recognition. J. Algorithms 1(4), 359–373 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  49. Shotton, J., Blake, A., Cipolla, R.: Multiscale categorical object recognition using contour fragments. IEEE Trans. PAMI 30(7), 1270–1281 (2008)

    Article  Google Scholar 

  50. Shu, X., Wu, X.J.: A novel contour descriptor for 2d shape matching and its application to image retrieval. Image Vis. Comput. 29(4), 286–294 (2011)

    Article  Google Scholar 

  51. Thureson, J., Carlsson, S.: Appearance based qualitative image description for object class recognition. In: ECCV, pp. 518–529 (2004)

  52. Tieng, Q.M., Boles, W.: Recognition of 2d object contours using the wavelet transform zero-crossing representation. IEEE Trans. PAMI 19(8), 910–916 (1997)

    Article  Google Scholar 

  53. van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Trans. PAMI 32(9), 1582–1596 (2010)

    Article  Google Scholar 

  54. Van Otterloo, P.J.: A Contour-Oriented Approach to Shape Analysis. Prentice Hall International Ltd., Hertfordshire (1991)

    MATH  Google Scholar 

  55. Wang, F., Kang, L., Li, Y.: Sketch-based 3d shape retrieval using convolutional neural networks. In: IEEE CVPR, pp. 1875–1883 (2015)

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

    Google Scholar 

  57. Wang, X., Feng, B., Bai, X., Liu, W., Jan Latecki, L.: Bag of contour fragments for robust shape classification. Pattern Recogn. 47(6), 2116–2125 (2014)

    Article  Google Scholar 

  58. Yang, C., Tiebe, O., Pietsch, P., Feinen, C., Kelter, U., Grzegorzek, M.: Shape-based object retrieval by contour segment matching. In: IEEE ICIP, pp. 2202–2206 (2014)

  59. Yang, C., Tiebe, O., Pietsch, P., Feinen, C., Kelter, U., Grzegorzek, M.: Shape-based object retrieval and classification with supervised optimisation. In: ICPRAM, pp. 204–211 (2015)

  60. Yang, H.S., Lee, S.U., Lee, K.M.: Recognition of 2d object contours using starting-point-independent wavelet coefficient matching. VCIR 9(2), 171–181 (1998)

    Google Scholar 

  61. Yang, M., Kpalma, K., Idiyo, R.J.: A survey of shape feature extraction techniques. In: Pattern Recognition, pp. 43–90 (2008)

  62. Young, I.T., Walker, J.E., Bowie, J.E.: An analysis technique for biological shape. I. Inf. Control 25(4), 357–370 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  63. Yule, G., Kendall, M.: An Introduction to the Theory of Statistic, 14th edn. Griffin, London, UK (1968)

    MATH  Google Scholar 

  64. Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recogn. 37(1), 1–19 (2004)

    Article  Google Scholar 

  65. Zhu, Q., Wang, L., Wu, Y., Shi, J.: Contour context selection for object detection: a set-to-set contour matching approach. In: ECCV, pp. 774–787 (2008)

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Acknowledgements

Research activities leading to this work have been supported by the China Scholarship Council (CSC) and the German Research Foundation (DFG) within the Research Training Group 1564 (GRK 1564).

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Authors and Affiliations

  1. Research Group for Pattern Recognition, Institute for Vision and Graphics, University of Siegen, Hoelderlinstr. 3, 57076, Siegen, Germany

    Cong Yang, Oliver Tiebe, Kimiaki Shirahama & Marcin Grzegorzek

  2. Laboratory of Operational Research and Artificial Intelligence, Institute of Computing Science, Poznan University of Technology, Pl. Marii Skłodowskiej-Curie, 60-965, Poznan, Poland

    Ewa Łukasik

  3. Faculty of Informatics and Communication, University of Economics in Katowice, Bogucicka 3, 40-226, Katowice, Poland

    Marcin Grzegorzek

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  1. Cong Yang
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Correspondence to Cong Yang.

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Yang, C., Tiebe, O., Shirahama, K. et al. Evaluating contour segment descriptors. Machine Vision and Applications 28, 373–391 (2017). https://doi.org/10.1007/s00138-017-0823-9

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