Region-Based Image Retrieval using Radial Basis Function Network

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.

• Type: Conference
• Year: 2006
• Where: ICMCS
• Authors: Kui Wu, Kim-Hui Yap, Lap-Pui Chau
• Keywords: ICMCS 2006, Radial Basis Function Network, Region-based Image, Unsupervised Subtractive Clustering