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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)

Abstract

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.

Keywords

SIFT Shape description Classification Image retrieval 

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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

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