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Dissimilarity-Based Classifications in Eigenspaces

  • Sang-Woon Kim
  • Robert P. W. Duin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

This paper presents an empirical evaluation on a dissimilarity measure strategy by which dissimilarity-based classifications (DBCs) [10] can be efficiently implemented. In DBCs, classifiers are not based on the feature measurements of individual objects, but rather on a suitable dissimilarity measure among the objects. In image classification tasks, however, one of the most intractable problems to measure the dissimilarity is the distortion and lack of information caused by the differences in illumination and directions and outlier data. To overcome this problem, in this paper, we study a new way of performing DBCs in eigenspaces spanned, one for each class, by the subset of principal eigenvectors, extracted from the training data set through a principal component analysis. Our experimental results, obtained with well-known benchmark databases, demonstrate that when the dimensionality of the eigenspaces has been appropriately chosen, the DBCs can be improved in terms of classification accuracies.

Keywords

Error Rate Face Recognition Face Image Dissimilarity Matrix Manhattan Distance 
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

  • Sang-Woon Kim
    • 1
  • Robert P. W. Duin
    • 2
  1. 1.Dept. of Computer Science and EngineeringMyongji UniversityYonginSouth Korea
  2. 2.Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyThe Netherlands

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