Cascaded neural networks based image classifier

Authors:
Changjing Shang;Keith Brown
Affiliations:
Dept. of Computing and Electrical Engineering, Heriot-Watt University, UK;Dept. of Computing and Electrical Engineering, Heriot-Watt University, UK
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Year:
1993

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Abstract

This paper presents a texture image classification system based upon the use of two cascaded multi-layer feedforward neural networks (MFNNs). The first network transforms a set of high-dimensional and correlated feature images into another set of uncorrelated principal feature images with its dimensionality being significantly compressed whilst minimising the information lost. The second accomplishes the task of feature pattern classification by using only those principal features obtained by the former. A synthesised training system for synchronously learning the weights of these two networks is also presented. Important advantages of both the classification system and the associated training system are described. They are further demonstrated by detailed examples.