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
Link To Document :
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