Image retrieval based on similarity learning
We explore the use of various learning algorithms to predict the user's measure of similarity between a given query image and images in a database. Our aim is to obtain a similarity coefficient, for use in image retrieval, that more accurately reflects that of the user. The performance of a variety of learning machines was evaluated using statistical resampling to estimate the prediction error and retrieval effectiveness. The proposed approach was demonstrated using synthetic shape and texture examples. The results of the study are very promising, especially those obtained by the general regression neural network and the support vector machine/radial basis function method.