  In this paper, we propose a new multi-layer structural approach for the task
of object based image retrieval. In our work we tackle the problem of
structural organization of local features. The structural features we propose
are nested multi-layered local graphs built upon sets of SURF feature points
with Delaunay triangulation. A Bag-of-Visual-Words (BoVW) framework is applied
on these graphs, giving birth to a Bag-of-Graph-Words representation. The
multi-layer nature of the descriptors consists in scaling from trivial Delaunay
graphs - isolated feature points - by increasing the number of nodes layer by
layer up to graphs with maximal number of nodes. For each layer of graphs its
own visual dictionary is built. The experiments conducted on the SIVAL and
Caltech-101 data sets reveal that the graph features at different layers
exhibit complementary performances on the same content and perform better than
baseline BoVW approach. The combination of all existing layers, yields
significant improvement of the object recognition performance compared to
single level approaches.
