SIFT Descriptor for Binary Shape Discrimination, Classification and Matching

  • Insaf Setitra
  • Slimane LarabiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)


In this work, we study efficiency of SIFT descriptor in discrimination of binary shapes. We also analyze how the use of \(2-tuples\) of SIFT keypoints can affect discrimination of shapes. The study is divided into two parts, the first part serves as a primary analysis where we propose to compute overlap of classes using SIFT and a majority vote of keypoints. In the second part, we analyze both classification and matching of binary shapes using SIFT and Bag of Features. Our empirical study shows that SIFT although being considered as a texture feature, can be used to distinguish shapes in binary images and can be applied to the classification of foreground’s silhouettes.


SIFT Shape description Classification Image retrieval 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Wang, X.: Intelligent Multi-camera Video Surveillance: A Review. Pattern Recogn. Lett. 34(1), 3–19 (2013)CrossRefGoogle Scholar
  3. 3.
    Tsuchiya, M., Fujiyoshi, H.: Evaluating feature importance for object classification in visual surveillance. In: 18th International Conference on Pattern Recognition, ICPR 2006Google Scholar
  4. 4.
    Song, Z., Chen, Q., Huang, Z., Hua, Y., Yan, S.: Contextualizing object detection and classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)Google Scholar
  5. 5.
    Zhang, Z., Li, M., Huang, K., Tan, T.: Boosting local feature descriptors for automatic objects classification in traffic scene surveillance. In: 19th International Conference on Pattern Recognition, ICPR 2008Google Scholar
  6. 6.
    Deselaers, T., Heigold, G., Ney, H.: Object Classification by Fusing SVMs and Gaussian Mixtures. Pattern Recogn. 43(7), 2476–2484 (2010)CrossRefzbMATHGoogle Scholar
  7. 7.
    Conde, C., Moctezuma, D., De Diego, I.M., Cabello, E.: HoGG: Gabor and HoG-based human detection for surveillance in non-controlled environments. Neurocomputing 100, 19–30 (2013)CrossRefGoogle Scholar
  8. 8.
    Chahooki, M.A.Z., Charkari, N.M.: Shape Classification by Manifold Learning in Multiple Observation Spaces. Inf. Sci. 262, 46–61 (2014)CrossRefGoogle Scholar
  9. 9.
    Nanni, L., Lumini, A., Brahnam, S.: Ensemble of different local descriptors, codebook generation methods and subwindow configurations for building a reliable computer vision system. Inf. Sci. 26(2), 89–100 (2014)Google Scholar
  10. 10.
    Torresani, L., Szummer, M., Fitzgibbon, A.: Efficient object category recognition using classemes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 776–789. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  11. 11.
    Bouagar, S., Larabi, S.: Efficient descriptor for full and partial shape matching. Multimedia Tools and Applications, 1–23 (2014). doi: 10.1007/s11042-014-2417-0
  12. 12.
    Lin, W.-S., Wu, Y.-L., Hung, W.-C., Tang, C.-Y.: A Study of Real-Time Hand Gesture Recognition Using SIFT on Binary Images. Advances in Intelligent Systems and Applications 2, 235–246Google Scholar
  13. 13.
    Ling, H., Jacobs, D.W.: Shape Classification Using the Inner-Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(2), 286–299Google Scholar
  14. 14.
    Setitra, I., Larabi, S.: Background subtraction algorithms with post-processing: a review. In: 22nd International Conference on Pattern Recognition (ICPR) (2014)Google Scholar
  15. 15.
    Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2003)Google Scholar
  16. 16.
    Latecki, L.J., Lakmper, R., Eckhardt, U.: Shape Descriptors for non-rigid shapes with a single closed contour. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2000)Google Scholar
  17. 17.
    Bai, X., Rao, C., Wang, X., Vocabulary, S.: A Robust and Efficient Shape Representation for Shape Matching. IEEE Transactions on Image Processing 23(9), 3935–3949 (2014)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Bai, X., Yang, X., Latecki, L.J., Liu, W., Tu, Z.: Learning Context-Sensitive Shape Similarity by Graph Transduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(5), 861–874 (2010)CrossRefGoogle Scholar
  19. 19.
    Seidenari, L., Serra, G., Bagdanov, A.D., Del Bimbo, A.: Local Pyramidal Descriptors for Image Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014 36(5), 1033–1040 (2013)CrossRefGoogle Scholar
  20. 20.
    Kontschieder, P., Donoser, M., Bischof, H.: Beyond pairwise shape similarity analysis. In: Zha, H., Taniguchi, R., Maybank, S. (eds.) ACCV 2009, Part III. LNCS, vol. 5996, pp. 655–666. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  21. 21.
    Ramesh Peter, B., Xiand, C., Lee, T.H.: Shape classification using invariant features and contextual information in the bag-of-words model. Pattern Recognition 48(3), 894–906 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Research Center on Scientific and Technical Information CeristBen AknounAlgeria
  2. 2.University of Science and Technology Houari BoumedieneBab EzzouarAlgeria

Personalised recommendations