A NEURAL NETWORK-BASED IMAGE RETRIEVAL USING NONLINEAR COMBINATION OF HETEROGENEOUS FEATURES

Abstract

In content-based image retrieval (CBIR), content of an image can be expressed in terms of different features such as color, texture, shape, or text annotations. Retrieval based on these features can be various by the way how to combine the feature values. Most of the existing approaches assume a linear relationship between different features, and the usefulness of such systems was limited due to the difficulty in representing high-level concepts using low-level features. In this paper, we introduce Neural Network-based Flexible Image Retrieval (NNFIR) system, a human-computer interaction approach to CBIR using Radial Basis Function (RBF) network to combine the values of the heterogeneous features. By using the RBF network, this approach determines nonlinear relationship between features so that more accurate similarity comparison between images can be supported. The experimental results show that the proposed approach has the superior retrieval performance than the existing linear combining method, the rank-based method and the BackPropagation-based method. Although the proposed retrieval model is for CBIR, it can be easily expanded to handle other media types, such as video and audio.