In this paper, the application of support vector machines (SVM) in relevance feedback for region-based image retrieval is investigated. Both the one class SVM as a class distribution estimator and two classes SVM as a classifier are taken into account. For the latter, two representative display strategies are studied. Since the common kernels often rely on inner product or L/sub p/ norm in the input space, they are infeasible in the region-based image retrieval systems that use variable-length representations. To resolve the issue, a new kind of kernel that is a generalization of Gaussian kernel is proposed. Experimental results on a database of 10,000 general-purpose images demonstrate the effectiveness and robustness of the proposed approach.