Title :
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.
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;
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
DOI :
10.1109/ICME.2006.262896
