Identifying Relevant Features of Images from Their 2-D Topology

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 398)

Abstract

Inspired by the abilities of the human brain to identify elements from eyesight, the problem of dimensionality reduction in the domain of visual perception consists to extract a few number of features from an image database in order to recognize them. This paper presents an innovative feature extractor for images that considers their two dimensional topology to identify their relevant features. Numerical experiment applied to 70000 pictures representing handwritten digits, 698 images illustrating the face of a person under different poses and lighting directions, or 355 color holidays photos demonstrate the accuracy of our approach to drastically reduce the dimension while conserving the intelligible relations between data objects, reaching a classification of better quality from the reduced version of images than from their original full-size realizations.

Keywords

Image reduction feature extractor classification visual perception 

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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.CIRRELT - Université de MontréalMontréalCanada

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