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 highlevel 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 subspace projection method. The empirical performance of the described dual diffusion model has been evaluated in terms of precision and recall on th...

• Type: Conference
• Year: 2006
• Where: MIR
• Authors: Serhiy Kosinov, Stéphane Marchand-Maillet, Igor Kozintsev, Carole Dulong, Thierry Pun
• Keywords: Activation, Diffusion Process, MIR 2006, Multimedia, final Activation