LISF: An Invariant Local Shape Features Descriptor Robust to Occlusion

Leonardo Chang, Miguel Arias-Estrada, L. Enrique Sucar, José Hernández-Palancar

2014

Abstract

In this work an invariant shape features extraction, description and matching method (LISF) for binary images is proposed. In order to balance the discriminative power and the robustness to noise and occlusion in the contour, local features are extracted from contour to describe shape, which are later matched globally. The proposed extraction, description and matching methods are invariant to rotation, translation, and scale and present certain robustness to partial occlusion. Its invariability and robustness are validated by the performed experiments in shape retrieval and classification tasks. Experiments were carried out in the Shape99, Shape216, and MPEG-7 datasets, where different artifacts were artificially added to obtain partial occlusion as high as 60%. For the highest occlusion levels the proposed method outperformed other popular shape description methods, with about 20% higher bull’s eye score and 25% higher accuracy in classification.

References

  1. Adamek, T. and O'Connor, N. E. (2004). A multiscale representation method for nonrigid shapes with a single closed contour. IEEE Trans. Circuits Syst. Video Techn., 14(5):742-753.
  2. Alajlan, N., Rube, I. E., Kamel, M. S., and Freeman, G. (2007). Shape retrieval using triangle-area representation and dynamic space warping. Pattern Recognition, 40(7):1911 - 1920.
  3. Bai, X., Yang, X., Latecki, L. J., Liu, W., and Tu, Z. (2010). Learning context-sensitive shape similarity by graph transduction. IEEE Trans. Pattern Anal. Mach. Intell., 32(5):861-874.
  4. Belongie, S., Malik, J., and Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4):509-522.
  5. Biederman, I. and Ju, G. (1988). Surface versus edge-based determinants of visual recognition. Cognitive Psychology, 20(1):38-64.
  6. Chetverikov, D. (2003). A Simple and Efficient Algorithm for Detection of High Curvature Points in Planar Curves. Proceedings of the 23rd Workshop of the Austrian Pattern Recognition Group, pages 746-753.
  7. Chong, C.-W., Raveendran, P., and Mukundan, R. (2004). Translation and scale invariants of legendre moments. Pattern Recognition, 37(1):119-129.
  8. De Winter, J. and Wagemans, J. (2004). Contour-based object identification and segmentation: stimuli, norms and data, and software tools. Behavior research methods instruments computers. A journal of the Psychonomic Society Inc, 36(4):604-624.
  9. Direkoglu, C. and Nixon, M. (2011). Shape classification via image-based multiscale description. Pattern Recognition, 44(9):2134-2146.
  10. Gonzalez-Aguirre, D. I., Hoch, J., Rhl, S., Asfour, T., Bayro-Corrochano, E., and Dillmann, R. (2011). Towards shape-based visual object categorization for humanoid robots. In ICRA, pages 5226-5232. IEEE.
  11. Khotanzad, A. and Hong, Y. H. (1988). Rotation invariant pattern recognition using zernike moments. Pattern Recognition, 1988., 9th International Conference on, pages 326-328 vol.1.
  12. Kim, W.-Y. and Kim, Y.-S. (2000). A region-based shape descriptor using zernike moments. Signal Processing: Image Communication, 16(12):95 - 102.
  13. Latecki, L. J., Lakmper, R., and Eckhardt, U. (2000). Shape descriptors for non-rigid shapes with a single closed contour. In CVPR, pages 1424-1429. IEEE Computer Society.
  14. McNeill, G. and Vijayakumar, S. (2006). Hierarchical procrustes matching for shape retrieval. In CVPR (1), pages 885-894. IEEE Computer Society.
  15. Mokhtarian, F. and Bober, M. (2003). Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. Kluwer.
  16. Sebastian, T. B., Klein, P. N., and Kimia, B. B. (2004). Recognition of shapes by editing their shock graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5):550-571.
  17. Shu, X. and Wu, X.-J. (2011). A novel contour descriptor for 2D shape matching and its application to image retrieval. Image and Vision Computing, 29(4):286- 294.
  18. Toshev, A., Taskar, B., and Daniilidis, K. (2011). Shapebased Object Detection via Boundary Structure Segmentation. International Journal of Computer Vision, 99(2):123-146.
  19. Trinh, N. H. and Kimia, B. B. (2011). Skeleton Search: Category-Specific Object Recognition andSegmentation Using a Skeletal Shape Model. International Journal of Computer Vision, 94(2):215-240.
  20. Wang, X., Bai, X., Ma, T., Liu, W., and Latecki, L. J. (2012). Fan shape model for object detection. In CVPR, pages 151-158. IEEE.
  21. Yang, X., Bai, X., Kknar-Tezel, S., and Latecki, L. (2013). Densifying distance spaces for shape and image retrieval. Journal of Mathematical Imaging and Vision, 46(1):12-28.
  22. Yang, X., Kknar-tezel, S., and Latecki, L. J. (2009). Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval. In In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR.
  23. Zhang, D. and Lu, G. (2002). Shape based image retrieval using generic fourier descriptors. In Signal Processing: Image Communication 17, pages 825-848.
Download


Paper Citation


in Harvard Style

Chang L., Arias-Estrada M., Sucar L. and Hernández-Palancar J. (2014). LISF: An Invariant Local Shape Features Descriptor Robust to Occlusion . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 429-437. DOI: 10.5220/0004825504290437


in Bibtex Style

@conference{icpram14,
author={Leonardo Chang and Miguel Arias-Estrada and L. Enrique Sucar and José Hernández-Palancar},
title={LISF: An Invariant Local Shape Features Descriptor Robust to Occlusion},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={429-437},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004825504290437},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - LISF: An Invariant Local Shape Features Descriptor Robust to Occlusion
SN - 978-989-758-018-5
AU - Chang L.
AU - Arias-Estrada M.
AU - Sucar L.
AU - Hernández-Palancar J.
PY - 2014
SP - 429
EP - 437
DO - 10.5220/0004825504290437