Image classification with the use of radial basis function neural networks and the minimization of the localized generalization error

Image classification arises as an important phase in the overall process of automatic image annotation and image retrieval. In this study, we are concerned with the design of image classifiers developed in the feature space formed by low level primitives defined in the setting of the MPEG-7 standard. Our objective is to investigate the discriminatory properties of such standard image descriptors and look at efficient architectures of the classifiers along with their design pursuits. The generalization capabilities of an image classifier are essential to its successful usage in image retrieval and annotation. Intuitively, it is expected that the classifier should achieve high classification accuracy on unseen images that are quite “similar” to those occurring in the training set. On the other hand, we may assume that the performance of the classifier could not be guaranteed in the case of images that are very much dissimilar from the elements of the training set. To follow this observation, we develop and use a concept of the localized generalization error and show how it guides the design of the classifier. As image classifier, we consider the usage of the radial basis function neural networks (RBFNNs). Through intensive experimentation we show that the resulting classifier outperforms other classifiers such as a multi-class support vector machines (SVMs) as well as “standard” RBFNNs (viz. those developed without the guidance offered by the optimization of the localized generalization error). The experimental studies reveal some interesting interpretation abilities of the RBFNN classifiers being related with their receptive fields.