Image receptive fields neural networks for object recognition

Authors:
Paméla Daum;Jean-Luc Buessler;Jean-Philippe Urban
Affiliations:
MIPS Laboratory, Université de Haute-Alsace, Mulhouse, France;MIPS Laboratory, Université de Haute-Alsace, Mulhouse, France;MIPS Laboratory, Université de Haute-Alsace, Mulhouse, France
Venue:
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Year:
2011

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Abstract

This paper extends a recent and very appealing approach of computational learning to the field of image analysis. Recent works have demonstrated that the implementation of Artificial Neural Networks (ANN) could be simplified by using a large amount of neurons with random weights. Only the output weights are adapted, with a single linear regression. Supervised learning is very fast and efficient. To adapt this approach to image analysis, the novelty is to initialize weights, not as independent random variables, but as Gaussian functions with only a few random parameters. This creates smooth random receptive fields in the image space. These Image Receptive Fields - Neural Networks (IRFNN) show remarkable performances for recognition applications, with extremely fast learning, and can be applied directly to images without pre-processing.