• DocumentCode
    2963973
  • Title

    Feature extractions with geometric algebra for classification of objects

  • Author

    Pham, Minh Tuan ; Tachibana, Kanta ; Hitzer, Eckhard ; Buchholz, Sven ; Yoshikawa, Tomohiro ; Furuhashi, Takeshi

  • fYear
    2008
  • fDate
    1-8 June 2008
  • Firstpage
    4070
  • Lastpage
    4074
  • Abstract
    Most conventional methods of feature extraction do not pay much attention to the geometric properties of data, even in cases where the data have spatial features. In this study we introduce geometric algebra to undertake various kinds of feature extraction from spatial data. Geometric algebra is a generalization of complex numbers and of quaternions, and it is able to describe spatial objects and relations between them. This paper proposes to use geometric algebra to systematically extract geometric features from data given in a vector space. We show the results of classification of hand-written digits, which were classified by feature extraction with the proposed method.
  • Keywords
    algebra; computational geometry; feature extraction; handwritten character recognition; pattern classification; feature extractions; geometric algebra; handwritten digit classification; object classification; Algebra; Coordinate measuring machines; Data mining; Feature extraction; Image processing; Machine learning; Multidimensional signal processing; Quaternions; Testing; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1820-6
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2008.4634383
  • Filename
    4634383