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Learning Features for Object Recognition
Yingqiang Lin and Bir Bhanu
Center for Research in Intelligent Systems
University of California
Riverside, CA, 92521, USA
{yqlin,bhanu}@vislab.ucr.edu
Abstract. Features represent the characteristics of objects and selecting or synthesizing effective composite features are the key factors to the performance of object recognition. In this paper, we propose a co-evolutionary genetic programming (CGP) approach to learn composite features for object recognition. The motivation for using CGP is to overcome the limitations of human experts who consider only a small number of conventional combinations of primitive features during synthesis. On the other hand, CGP can try a very large number of unconventional combinations and these unconventional combinations may yield exceptionally good results in some cases. Our experimental results with real synthetic aperture radar (SAR) images show that CGP can learn good composite features. We show results to distinguish objects from clutter and to distinguish objects that belong to several classes.
LNCS 2724, p. 2227 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003