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.
Similar content being viewed by others
References
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)
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)
Andrew, A.: Another efficient algorithm for convex hulls in two dimensions. Inf. Process. Lett. 9(5), 216–219 (1979)
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)
Bai, X., Latecki, L., Liu, W.: Skeleton pruning by contour partitioning with discrete curve evolution. IEEE Trans. PAMI 29(3), 449–462 (2007)
Baust, M., Demaret, L., Storath, M., Navab, N., Weinmann, A.: Total variation regularization of shape signals. In: IEEE CVPR, pp. 2075–2083 (2015)
Bellman, R.: The theory of dynamic programming. Bull. Am. Math. Soc. 60(6), 503–516 (1954)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. PAMI 24(4), 509–522 (2002)
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)
Bhattacharyya, A.: On a measure of divergence between two multinomial populations. Indian J. Stat. 7(4), 401–406 (1946)
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)
Burkard, R.E., Dell’Amico, M., Martello, S.: Assignment Problems, Revised Reprint, pp. 93–99. SIAM (2009)
Chellappa, R., Bagdazian, R.: Fourier coding of image boundaries. IEEE Trans. PAMI 6(1), 102–105 (1984)
Chen, L., Feris, R., Turk, M.: Efficient partial shape matching using smith-waterman algorithm. In: CVPR, pp. 1–6 (2008)
Cootes, T.F., Cooper, D., Taylor, C., Graham, J.: Trainable method of parametric shape description. Image Vis. Comput. 10(5), 289–294 (1992)
Daliri, M.R., Torre, V.: Robust symbolic representation for shape recognition and retrieval. Pattern Recogn. 41(5), 1782–1798 (2008)
Daliri, M.R., Torre, V.: Classification of silhouettes using contour fragments. Comput. Vis. Image Underst. 113(9), 1017–1025 (2009)
Daliri, M.R., Torre, V.: Shape recognition based on kernel-edit distance. Comput. Vis. Image Underst. 114(10), 1097–1103 (2010)
de Junior, Mesquita Sa J.J., Backes, A.R.: Shape classification using line segment statistics. Inf. Sci. 305, 349–356 (2015)
Donoser, M., Riemenschneider, H., Bischof, H.: Efficient partial shape matching of outer contours. In: ACCV, pp. 281–292 (2010)
Eitz, M., Richter, R., Boubekeur, T., Hildebrand, K., Alexa, M.: Sketch-based shape retrieval. ACM Graph. 31(4), 1–10 (2012)
Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Ferrari, V., Tuytelaars, T., Gool, L.V.: Object detection by contour segment networks. In: ECCV, pp. 14–28 (2006)
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)
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)
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)
Karczmarek, P., Kiersztyn, A., Pedrycz, W., Rutka, P.: Chain code-based local descriptor for face recognition. In: CORES, pp. 10–20 (2015)
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)
Kontschieder, P., Riemenschneider, H., Donoser, M., Bischof, H.: Discriminative learning of contour fragments for object detection. In: BMVC, pp. 1–12 (2011)
Krzyzak, A., Leung, S., Suen, C.: Reconstruction of two-dimensional patterns from fourier descriptors. Mach. Vis. Appl. 2(3), 123–140 (1989)
Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logist. Q. 2, 83–97 (1955)
Kurtzberg, J.M.: On approximation methods for the assignment problem. J. ACM 9(4), 419–439 (1962)
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)
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)
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)
Lu, C., Latecki, L., Adluru, N., Yang, X., Ling, H.: Shape guided contour grouping with particle filters. In: IEEE ICCV, pp. 2288–2295 (2009)
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)
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)
Maheshwari, A., Sack, J.R., Shahbaz, K., Zarrabi-Zadeh, H.: Improved algorithms for partial curve matching. Algorithmica 69(3), 641–657 (2014)
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)
Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285–296), 23–27 (1975)
Payet, N., Todorovic, S.: From a set of shapes to object discovery. In: ECCV, pp. 57–70 (2010)
Peura, M., Iivarinen, J.: Efficiency of simple shape descriptors. In: Aspects of visual form, pp. 443–451 (1997)
Plackett, R.L.: Karl Pearson and the chi-squared test. Int. Stat. Rev. 51(1), 5972 (1983)
Riemenschneider, H., Donoser, M., Bischof, H.: Using partial edge contour matches for efficient object category localization. In: ECCV, pp. 29–42 (2010)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. IJCV 40(2), 99–121 (2000)
Salvador, S., Chan, P.: Fastdtw: toward accurate dynamic time warping in linear time and space. In: KDD, pp. 70–80 (2004)
Sellers, P.H.: The theory and computation of evolutionary distances: pattern recognition. J. Algorithms 1(4), 359–373 (1980)
Shotton, J., Blake, A., Cipolla, R.: Multiscale categorical object recognition using contour fragments. IEEE Trans. PAMI 30(7), 1270–1281 (2008)
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)
Thureson, J., Carlsson, S.: Appearance based qualitative image description for object class recognition. In: ECCV, pp. 518–529 (2004)
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)
van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Trans. PAMI 32(9), 1582–1596 (2010)
Van Otterloo, P.J.: A Contour-Oriented Approach to Shape Analysis. Prentice Hall International Ltd., Hertfordshire (1991)
Wang, F., Kang, L., Li, Y.: Sketch-based 3d shape retrieval using convolutional neural networks. In: IEEE CVPR, pp. 1875–1883 (2015)
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)
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)
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)
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)
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)
Yang, M., Kpalma, K., Idiyo, R.J.: A survey of shape feature extraction techniques. In: Pattern Recognition, pp. 43–90 (2008)
Young, I.T., Walker, J.E., Bowie, J.E.: An analysis technique for biological shape. I. Inf. Control 25(4), 357–370 (1974)
Yule, G., Kendall, M.: An Introduction to the Theory of Statistic, 14th edn. Griffin, London, UK (1968)
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recogn. 37(1), 1–19 (2004)
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)
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).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-017-0823-9