Abstract
Shape context descriptors have been a valuable tool in shape description since their introduction. In this paper we examine the performance of shape context descriptors in the presence of noisy human silhouette data. Shape context descriptors have been shown to be robust to Gaussian noise in the task of shape matching. We implement four different configurations of shape context by altering the spacing of the histogram bins and then test the performance of these configurations in the presence of noise. The task used for these tests is recognition of body part shapes in human silhouettes. The noise in human silhouettes is principally from three sources: the noise from errors in silhouette segmentation, noise from loose clothing and noise from occlusions. We show that in the presence of this noise a newly proposed spacing for the shape context histogram bins has the best performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agarwal, A., Triggs, B.: Recovering 3d human pose from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1052–1062 (2006)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)
Belongie, S., Malik, J., Puzicha, J.: Shape context: A new descriptor for shape matching and object recognition. In: NIPS, pp. 831–837 (2000)
Bregler, C., Malik, J.: Tracking people with twists and exponential maps. In: CVPR 1998: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, p. 8. IEEE Computer Society, Los Alamitos (1998)
Chui, H., Rangarajan, A.: A new algorithm for non-rigid point matching. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2000)
Körtgen, M., Park, G.-J., Novotni, M., Klein, R.: 3d shape matching with 3d shape contexts. In: Proc. 7th Central European Seminar on Computer Graphics, Budmerice, Slovakia (April 2003)
Li, J., Zhou, S.K., Chellappa, R.: Appearance modeling under geometric context. In: ICCV 2005: Proceedings of the Tenth IEEE International Conference on Computer Vision, Washington, DC, USA, pp. 1252–1259. IEEE Computer Society, Los Alamitos (2005)
Barnard, M., Matilainen, M., Heikkilä, J.: Body part segmentation of noisy human silhouette images. In: Proc. IEEE International Conference on Multimedia and Expo. (ICME 2008), Hannover, Germany (2008)
Mittal, A., Zhao, L., Davis, L.S.: Human body pose estimation using silhouette shape analysis. In: Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 263–270 (July 2003)
Mori, G., Malik, J.: Recovering 3d human body configurations using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1052–1062 (2006)
Poppe, R., Poel, M.: Comparison of silhouette shape descriptors for example-based human pose recovery. In: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (2006)
Rabiner, L.R.: A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Sidenbladh, H., Black, M.J., Sigal, L.: Implicit probabilistic models of human motion for synthesis and tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 784–800. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barnard, M., Heikkilä, J. (2008). On Bin Configuration of Shape Context Descriptors in Human Silhouette Classification. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_77
Download citation
DOI: https://doi.org/10.1007/978-3-540-88458-3_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88457-6
Online ISBN: 978-3-540-88458-3
eBook Packages: Computer ScienceComputer Science (R0)