K. Messer et J. Kittler, A COMPARISON OF COLOR TEXTURE ATTRIBUTES SELECTED BY STATISTICAL FEATURE-SELECTION AND NEURAL-NETWORK METHODS, Pattern recognition letters, 18(11-13), 1997, pp. 1241-1246
In this paper, two methods for selecting input features for a neural n
etwork used to aid iconic retrieval in an image database are presented
and compared. The first method involves training the network on all t
he feature inputs and then analysing the weight values in an attempt t
o find the more important input features. The second borrows a method
from statistical feature selection known as the sequential floating fo
rward selection algorithm. Both methods improve the computational effi
ciency of the image database search whilst not affecting the quality o
f the results obtained. Being more computationally efficient, network
weight analysis offers an attractive alternative to the statistical me
thod. (C) 1997 Elsevier Science B.V.