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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)

Abstract

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.

Keywords

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

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

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

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