Constructive learning algorithm-based RBF network for relevance feedback in image retrieval

Authors:
Fang Qian;Bo Zhang;Fuzong Lin
Affiliations:
State key laboratory of Intelligent Technology and Systems, Tsinghua University and Department of Computer Science and Technology, Tsinghua University, Beijing, China;State key laboratory of Intelligent Technology and Systems, Tsinghua University and Department of Computer Science and Technology, Tsinghua University, Beijing, China;State key laboratory of Intelligent Technology and Systems, Tsinghua University and Department of Computer Science and Technology, Tsinghua University, Beijing, China
Venue:
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Year:
2003

Citing 7
Cited 3

The nature of statistical learning theory

The nature of statistical learning theory
Three learning phases for radial-basis-function networks

Neural Networks
MindReader: Querying Databases Through Multiple Examples

VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers

IEEE Transactions on Signal Processing
The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments

IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval

IEEE Transactions on Circuits and Systems for Video Technology
A geometrical representation of McCulloch-Pitts neural model and its applications

IEEE Transactions on Neural Networks

Interactive Image Retrieval in a Fuzzy Framework

WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
The state of the art in image and video retrieval

CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Image retrieval using fuzzy relevance feedback and validation with MPEG-7 content descriptors

PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

It has been generally acknowledged that relevance feedback in image retrieval can be considered as a two-class learning and classification process. The classifier used is essential to the performance of relevance feedback. In this paper, a RBF neural network is employed during the relevance feedback process. The architecture of the RBF network is automatically determined by the constructive learning algorithm (CLA). The weights in the output layer of the network are learned by Least-mean-square method. Experiment results on 10,000 heterogeneous images demonstrate the proposed CLA-based RBF network can achieve comparable performance with support vector machines and support vector learning based RBF during the relevance feedback process. Furthermore, a practical advantage of the CLA-based RBF network is that the width of Gaussian kernel does not need to manually set while for SVM it need to be predefined according to experience.