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2006 IEEE International Conference on Multimedia and Expo
Region-Based Image Retrieval using Radial Basis Function Network
Toronto, ON, Canada
July 09-July 12
ISBN: 1-4244-0366-7
ASCII Text
x 
 
Kui Wu, Kim-hui Yap, Lap-pui Chau, "Region-Based Image Retrieval using Radial Basis Function Network," Multimedia and Expo, IEEE International Conference on, pp. 1777-1780, 2006 IEEE International Conference on Multimedia and Expo, 2006.
 
 
BibTex
x
 
@article{ 10.1109/ICME.2006.262896,
author = {Kui Wu and Kim-hui Yap and Lap-pui Chau},
title = {Region-Based Image Retrieval using Radial Basis Function Network},
journal ={Multimedia and Expo, IEEE International Conference on},
volume = {0},
year = {2006},
isbn = {1-4244-0366-7},
pages = {1777-1780},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICME.2006.262896},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Multimedia and Expo, IEEE International Conference on
TI - Region-Based Image Retrieval using Radial Basis Function Network
SN - 1-4244-0366-7
SP1777
EP1780
A1 - Kui Wu,
A1 - Kim-hui Yap,
A1 - Lap-pui Chau,
PY - 2006
KW - null
VL - 0
JA - Multimedia and Expo, IEEE International Conference on
ER -
 
Kui Wu, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Kim-hui Yap, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Lap-pui Chau, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
This paper presents a new framework that integrates relevance feedback into region-based image retrieval (RBIR) systems based on radial basis function network (RBFN). A modified unsupervised subtractive clustering algorithm is proposed for RBFN center selection according to the characteristics of region-based image representation. A new kernel function of RBFN is introduced for image similarity comparison under region-based representation. The underlying network parameters (weight and width) are then optimized using a supervised gradient-descent training strategy. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed hybrid learning approach.
Citation:
Kui Wu, Kim-hui Yap, Lap-pui Chau, "Region-Based Image Retrieval using Radial Basis Function Network," icme, pp.1777-1780, 2006 IEEE International Conference on Multimedia and Expo, 2006
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