Network-dependent kernels for image ranking

The exponential growth of social networks (SN) currently makes them the standard way to share and explore data where users put informations (images, text, audio,...) and refer to other contents. This creates connected networks whose links provide valuable informations in order to enhance the performance of many tasks in information retrieval including ranking and annotation. We introduce in this paper a novel image retrieval framework based on a new class of kernels referred to as "network-dependent". The main contribution of our method includes (i) a variational framework which helps designing a kernel using both the intrinsic image features and the underlying contextual informations resulting from different (e.g. social) links and (ii) the proof of convergence of the kernel to a fixed-point, that is positive definite and thus associated with a reproducing kernel Hilbert space (RKHS). Experiments conducted on different ground truths, including the ImageClef/Flickr set, show the outperformance and the substantial gain of our ranking kernel with respect to the use of classic "network-free" kernels.