A Neural Network-Based Image Retrieval Using Nonlinear Combination of Heterogeneous Features

* This work was supported by the Brain Korea 21 Project. AbstractIn 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 lowlevel features. In this paper, we introduce Neural Network-based Flexible Image Retrieval (NNFIR) system, a humancomputer 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 captures the user’s perception subjectivity more precisely using the dynamically updated weights.