Region-Based Image Retrieval using Radial Basis Function Network

Author

Wu, Kui ; Yap, Kim-Hui ; Chau, Lap-Pui

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ.

fYear

2006

fDate

9-12 July 2006

Firstpage

1777

Lastpage

1780

Abstract

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

Keywords

gradient methods; image retrieval; learning (artificial intelligence); radial basis function networks; relevance feedback; RBFN; RBIR system; hybrid learning approach; radial basis function network; region-based image retrieval; relevance feedback; supervised gradient-descent training strategy; unsupervised subtractive clustering algorithm; Clustering algorithms; Content based retrieval; Feature extraction; Feedback; Image representation; Image retrieval; Image segmentation; Kernel; Radial basis function networks; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia and Expo, 2006 IEEE International Conference on

Conference_Location

Toronto, Ont.

Print_ISBN

1-4244-0366-7

Electronic_ISBN

1-4244-0367-7

Type

conf

DOI

10.1109/ICME.2006.262896

Filename

4036965