Inexact graph matching based on kernels for object retrieval in image databases

Justine Lebrun (1) , Philippe-Henri Gosselin (2) , Sylvie Philipp-Foliguet (1)

1 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
2 MIDI - Multimedia Indexation and Data Integration

In the framework of online object retrieval with learning, we address the problem of graph matching using kernel functions. An image is represented by a graph of regions where the edges represent the spatial relationships. Kernels on graphs are built from kernel on walks in the graph. This paper firstly proposes new kernels on graphs and on walks, which are very efficient for graphs of regions. Secondly we propose fast solutions for exact or approximate computation of these kernels. Thirdly we show results for the retrieval of images containing a specific object with the help of very few examples and counter-examples in the framework of an active retrieval scheme.

Keywords: Online; Interactive; Database; Content-Based; Object Retrieval; Image Retrieval; Machine Learning; Kernel Methods; Graph matching; Inexact match

Domains: Machine Learning [stat.ML]; Information Retrieval [cs.IR]

Justine Lebrun, Philippe-Henri Gosselin, Sylvie Philipp-Foliguet. Inexact graph matching based on kernels for object retrieval in image databases. Image and Vision Computing, 2011, 29 (11), pp.716-729. ⟨10.1016/j.imavis.2011.07.008⟩. ⟨hal-00660300⟩