Title :
A neural network-based image retrieval using nonlinear combination of heterogeneous features
Author :
Lee, Hyoung K. ; Yoo, Suk I.
Author_Institution :
Dept. of Comput. Sci., Seoul Nat. Univ., South Korea
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 captures the user´s perception subjectivity more precisely using the dynamically updated weights
Keywords :
content-based retrieval; radial basis function networks; color; content-based image retrieval; heterogeneous features; human-computer interaction; neural network-based image retrieval; nonlinear combination; nonlinear relationship; radial basis function network; shape; similarity comparison; text annotations; texture; Biological neural networks; Boolean functions; Content based retrieval; Data structures; Humans; Image databases; Image retrieval; Neural networks; Radial basis function networks; Shape;
Conference_Titel :
Evolutionary Computation, 2000. Proceedings of the 2000 Congress on
Conference_Location :
La Jolla, CA
Print_ISBN :
0-7803-6375-2
DOI :
10.1109/CEC.2000.870362