Distance-Free Image Retrieval Based on Stochastic Diffusion over Bipartite Graphs

We propose an approach to image retrieval that does not require any distance computations. The idea is to represent images and corresponding image features by means of the two sets of vertices of a bipartite graph. Even though in such a graph the images are not directly related, the degrees to which the features are present in an image allow for defining partial orders. If the degrees of presence are normalized such that they form probability distributions, similarity rankings result from the stationary distributions of stochastic diffusion processes over the graph. The method is closely related to recent approaches to ranking on manifolds but does not involve the computation of parameterized affinity and Laplacian matrices. Experiments with a standard image retrieval data set demonstrate the efficacy of the approach. Compared to a corresponding distance-based approach, it yields a higher overall precision.