An efficient radial basis function network approach for content-based image retrieval

In this paper, an efficient approach using radial basis function network (RBFN) with online learning capability is proposed for interactive content-based image retrieval (CBIR) systems. Based on the users' feedback, an RBFN is constructed, and the underlying parameters and network structure are adjusted adaptively using a training strategy. To capture the users' perceptual consistency in similarity, an error function is expressed in terms of accumulated training samples across all feedback sessions. Experimental results using a database of 10000 images demonstrate the effectiveness of the proposed method.