Cascaded neural networks based image classifier

Changjing Shang, Keith Brown

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationPlenary, Special, Audio, Underwater Acoustics, VLSI, Neural Networks
PagesI-617-I-619
Volume1
Publication statusPublished - 1993
Event18th IEEE International Conference on Acoustics, Speech and Signal Processing 1993 - Minneapolis, MN, United States
Duration: 27 Apr 199330 Apr 1993

Conference

Conference18th IEEE International Conference on Acoustics, Speech and Signal Processing 1993
CountryUnited States
CityMinneapolis, MN
Period27/04/9330/04/93

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  • Cite this

    Shang, C., & Brown, K. (1993). Cascaded neural networks based image classifier. In Plenary, Special, Audio, Underwater Acoustics, VLSI, Neural Networks (Vol. 1, pp. I-617-I-619)