A COMPARISON OF COLOR TEXTURE ATTRIBUTES SELECTED BY STATISTICAL FEATURE-SELECTION AND NEURAL-NETWORK METHODS

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.