Discriminant Basis for Object Classification

  • Authors:

  • Affiliations:

  • Venue:
  • ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
  • Year:
  • 2001

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Abstract

Abstract: This paper presents a technique to obtain a set of discriminant basis in an unsupervised way. A non-negative matrix factorization (NMF) is applied over a set of color newspapers to obtain a reduced space only considering positive constraints. This method is compared with the well-known Principal Component Analysis (PCA) obtaining promising results in the task of representing independent behaviours of the input data. With this methodology, we are able to find an ordered list of the basis functions being possible to select some of them for a further discriminant task. Moreover, the method can also be applied to the task of automatically extract object classes from a set of objects.