An improved radial basis function neural network for object image retrieval

Radial Basis Function Neural Networks (RBFNNs) have been widely used for classification and function approximation tasks. Hence, it is worthy to try improving and developing new learning algorithms for RBFNNs in order to get better results. This paper presents a new learning method for RBFNNs. An improved algorithm for center adjustment of RBFNNs and a novel algorithm for width determination have been proposed to optimize the efficiency of the Optimum Steepest Decent (OSD) algorithm. To initialize the radial basis function units more accurately, a modified approach based on Particle Swarm Optimization (PSO) is presented. The obtained results show fast convergence speed, better and same network response in fewer train data which states the generalization power of the improved neural network. The Improved PSO–OSD and Three-phased PSO–OSD algorithms have been tested on five benchmark problems and the results have been compared. Finally, using the improved radial basis function neural network we propose a new method for object image retrieval. The images to be retrieved are object images that can be divided into foreground and background. Experimental results show that the proposed method is really promising and achieves high performance.