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Effects of the number of hidden nodes used in a structured-based neural network on the reliability of image classification

Content Provider	Springer Nature Link
Author	Zou, Weibao Li, Yan Tang, Arthur
Copyright Year	2008
Abstract	A structured-based neural network (NN) with backpropagation through structure (BPTS) algorithm is conducted for image classification in organizing a large image database, which is a challenging problem under investigation. Many factors can affect the results of image classification. One of the most important factors is the architecture of a NN, which consists of input layer, hidden layer and output layer. In this study, only the numbers of nodes in hidden layer (hidden nodes) of a NN are considered. Other factors are kept unchanged. Two groups of experiments including 2,940 images in each group are used for the analysis. The assessment of the effects for the first group is carried out with features described by image intensities, and, the second group uses features described by wavelet coefficients. Experimental results demonstrate that the effects of the numbers of hidden nodes on the reliability of classification are significant and non-linear. When the number of hidden nodes is 17, the classification rate on training set is up to 95%, and arrives at 90% on the testing set. The results indicate that 17 is an appropriate choice for the number of hidden nodes for the image classification when a structured-based NN with BPTS algorithm is applied.
Starting Page	249
Ending Page	260
Page Count	12
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	18
Issue Number	3
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-02-27
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hidden nodes Backpropagation through structure Image classification Neural network Features set Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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Structure-Based Neural Network Classification for Panchromatic IKONOS Image Using Wavelet-Based Features

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