Image retrieval with graph kernel on regions

In the framework of the interactive search in image databases, we are interested in similarity measures able to learn during the search and usable in real-time. Images are represented by adjacency graphs of regions. In order to compare attributed graphs, we employ kernels on graphs built on sets of paths. In this paper, we introduce a fast kernel function whose similarity is based on several matches. We also introduce new features for edges in the graph. Experiments on a specific database having objects with heterogeneous backgrounds show the performance of our object retrieval technique.