In this paper, a language model adapted to graph-based representation of image content is proposed and assessed. The full indexing and retrieval processes are evaluated on two different image corpora. We show that using the spatial relationships with graph model has a positive impact on the results of standard Language Model (LM) and outperforms the baseline built upon the current state-of-the-art Support Vector Machine (SVM) classification method.

In this paper, we revisit the dependence language modelfor information retrieval proposed in [1], and show that thismodel is deficient from a theoretical point of view. We thenpropose a new model, well founded theoretically, for integratingdependencies between terms in the language model.This new model is simpler, yet more general, than the oneproposed in [1], and yields similar results in our experiments,on both syntactic and semantic dependencies.