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Margin Maximizing Discriminant Analysis for Multi-shot Based Object Recognition

  • Hui Kong
  • Eam Khwang Teoh
  • Pengfei Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)

Abstract

This paper discusses general object recognition by using image set in the scenario where multiple shots are available for each object. As a way of matching sets of images, canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the classical parametric distribution-based and non-parametric sample-based methods. However, it is essentially an representative but not a discriminative way for all the previous methods in using canonical correlations for comparing sets of images. Our purpose is to define a transformation such that, in the transformed space, the sum of canonical correlations (the cosine value of the principle angles between any two subspaces) of the intra-class image sets can be minimized and meantime the sum of canonical correlations of the inter-class image sets can be maximized. This is done by learning a margin-maximized linear discriminant function of the canonical correlations. Finally, this transformation is derived by a novel iterative optimization process. In this way, a discriminative way of using canonical correlations is presented. The proposed method significantly outperforms the state-of-the-art methods for two different object recognition problems on two large databases: a celebrity face database which is constructed using Image Google and the ALOI database of generic objects where hundreds of sets of images are taken at different views.

Keywords

Discriminant Analysis Face Recognition Linear Discriminant Analysis Recognition Rate Face Image 
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 2006

Authors and Affiliations

  • Hui Kong
    • 1
  • Eam Khwang Teoh
    • 2
  • Pengfei Xu
    • 1
  1. 1.Panasonic Singapore LabsSingapore
  2. 2.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore

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