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
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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