Dual diffusion model of spreading activation for content-based image retrieval

This paper introduces a content-based information retrieval method inspired by the ideas of spreading activation models. In response to a given query,the proposed approach computes document ranks as their final activation values obtained upon completion of a diffusion process. This diffusion process,in turn,is dual in the sense that it models the spreading of the query 's initial activation simultaneously in two similarity domains: low-level feature-based and high-level semantic.The formulation of the diffusion process relies on an approximation that makes it possible to compute the final activation as a solution to a linear system of differential equations via a matrix exponential without the need to resort to an iterative simulation.The latter calculation is performed efficiently by adapting a sparse routine based on Krylov sub-space projection method.The empirical performance of the described dual diffusion model has been evaluated in terms of precision and recall on the task of content-based digital image retrieval in query-by-example scenario. The obtained experimental results demonstrate that the proposed method achieves better overall performance compared to traditional feature-based approaches. This performance improvement is attained not only when both similarity domains are used, but also when a diffusion model operates only on the feature-based similarities.