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Class-Specific Low-Dimensional Representation of Local Features for Viewpoint Invariant Object Recognition

  • Bisser Raytchev
  • Yuta Kikutsugi
  • Toru Tamaki
  • Kazufumi Kaneda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

In this paper we propose a new general framework to obtain more distinctive local invariant features by projecting the original feature descriptors into low–dimensional feature space, while simultaneously incorporating also class information. In the resulting feature space, the features from different objects project to separate areas, while locally the metric relations between features corresponding to the same object are preserved. The low–dimensional feature embedding is obtained by a modified version of classical Multidimensional Scaling, which we call supervised Multidimensional Scaling (sMDS). Experimental results on a database containing images of several different objects with large variation in scale, viewpoint, illumination conditions and background clutter support the view that embedding class information into the feature representation is beneficial and results in more accurate object recognition.

Keywords

Linear Discriminant Analysis Multidimensional Scaling Principle Component Analysis Sift Descriptor Local Image Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bisser Raytchev
    • 1
  • Yuta Kikutsugi
    • 1
  • Toru Tamaki
    • 1
  • Kazufumi Kaneda
    • 1
  1. 1.Department of Information EngineeringHiroshima UniversityJapan

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