Image retrieval based on spatial context with Relaxed Gabriel Graph pyramid

Imposing the coherence of the spatial context on local features is becoming a necessity for object retrieval and recognition. Motivated by the success of proximity graphs in topological decomposition, clustering, and gradient estimation, we introduce a variation on and a generalization of Delaunay Triangulation, called a Relaxed Gabriel Graph (RGG), as the apex of spatial neighborhood association and design a Centrality-Sensitive Pyramid (CSP) model for hierarchical spatial context modeling. RGG is parameterized, and so allows the tuning of various applications and datasets. CSP achieves better neighborhood association and is more robust as regards feature description error than other related work. Our method is evaluated on Flickr Logos 32, Holiday, and Oxford Buildings benchmarks. Experimental results and comparisons demonstrate the superiority of our method in an image retrieval scenario.