HOOSC128: A More Robust Local Shape Descriptor

  • Edgar Roman-Rangel
  • Stephane Marchand-Maillet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8495)


This work introduces a new formulation of the Histogram-of-Orientations Shape-Context (HOOSC) descriptor [9], which has shorter dimensionality and higher degree of scale and rotation invariance with respect to previous formulations. We compare the performance of our proposed formulation in terms of dimensionality, computation time, robustness against scale and rotations transformations, retrieval precision and classification accuracy. Our results show that our approach outperforms previous formulations in all cases. We also propose the use of a normalized χ 2 test to compare the robustness of descriptors of different dimensionality against scale and rotations transformations.


Binary images local shape descriptor content-based image retrieval image classification 


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  3. 3.
    Eitz, M., Hays, J., Alexa, M.: How Do Humans Sketch Objects? ACM Transactions on Graphics (TOG) – SIGGRAPH 2012 Conference Proc. 31(4), 44 (2012)Google Scholar
  4. 4.
    Ferrari, V., Fevrier, L., Jurie, F., Schmid, C.: Groups of Adjacent Contours for Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(1), 36–51 (2008)CrossRefGoogle Scholar
  5. 5.
    Lloyd, S.: Least square quantization in PCM. IEEE Transactions on Information Theory 28(2), 129–137 (1982)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  7. 7.
    Mori, G., Belongie, S., Malik, J.: Efficient Shape Matching Using Shape Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(11), 1832–1837 (2005)CrossRefGoogle Scholar
  8. 8.
    Quelhas, P., Monay, F., Odobez, J.-M., Gatica-Perez, D., Tuytelaars, T.: A Thousand Words in a Scene. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(9), 1575–1589 (2007)CrossRefGoogle Scholar
  9. 9.
    Roman-Rangel, E., Pallan, C., Odobez, J.-M., Gatica-Perez, D.: Analyzing Ancient Maya Glyph Collections with Contextual Shape Descriptors. International Journal in Computer Vision, Special Issue in Cultural Heritage and Art Preservation 94(1), 101–117 (2011)Google Scholar
  10. 10.
    Roman-Rangel, E., Pallan, C., Odobez, J.-M., Gatica-Perez, D.: Searching the Past: An Improved Shape Descriptor to Retrieve Maya Hieroglyphs. In: ACM International Conference in Multimedia (2011)Google Scholar
  11. 11.
    Roman-Rangel, E., Marchand-Maillet, S.: Stopwords Detection in Bag-of-Visual-Words: The Case of Retrieving Maya Hieroglyphs. In: International Workshop on Multimedia for Cultural Heritage, The International Conference on Image Analysis and Processing (2013)Google Scholar
  12. 12.
    Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: IEEE International Conference on Computer Vision (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Edgar Roman-Rangel
    • 1
  • Stephane Marchand-Maillet
    • 1
  1. 1.CVMLabUniversity of GenevaSwitzerland

Personalised recommendations