Adaptive algorithms for hypergraph learning

Abstract

Social media sharing platforms enable image content as well as context information (e.g., user friendships, geo-tags assigned to images) to be jointly analyzed in order to achieve accurate image annotation or successful image recommendation. The context information is expressed frequently in terms of high-order relations, such as the relations among users, tags, and images. Hypergraphs can model the aforementioned high-order relations between their vertices (i.e., users, user social groups, tags, geo-tags, and images) by hyper-edges, whose influence can be assessed by properly estimating their weights. Here, an efficient adaptive hypergraph weight estimation is proposed for image tagging. In particular, both equality and inequality constraints enforced during hypergraph learning are taken into account and an efficient adaptation step selection using the Armijo rule is proposed. Experiments conducted on a dataset demonstrate the superior performance of the proposed approach compared to the state-of-the-art.