In the context of content based image retrieval, one of the most common approach nowadays is the Bag of Words (BoW) approach applied to local features such as textons, SIFT or SURF points etc. A dictionary is built by clustering the local features of a learning set of images. Then, for a test image each feature extracted is quantified according to the dictionary yielding a distribution of the features according to the dictionary. This method does not take into account any spatial relations between the features.
We introduce a semi-local feature approach called Delaunay graph feature. We use SURF points as nodes of a graph built be a Delaunay triangulation and then compute a dictionary graph words by a two pass hierarchic agglomerative clustering. The presentation will posit the premises and first experimental results of this work.