A new proposal for graph-based image classification using frequent approximate subgraphs

• We propose a new framework for image classification, which uses frequent approximate subgraph patterns as features.
• We propose to compute automatically the substitution matrices needed in the process, instead of using expert knowledge.
• We propose to use a new graph-based image representation.
• We propose a criterion for selecting isomorphism threshold for the graph mining process.

Graph-based data representations are an important research topic due to the suitability of this kind of data structure to model entities and the complex relations among them. In computer vision, graphs have been used to model images in order to add some high level information (relations) to the low-level representation of individual parts. How to deal with these representations for specific tasks is not easy due to the complexity of the data structure itself. In this paper we propose to use a graph mining technique for image classification, introducing approximate patterns discovery in the mining process in order to allow certain distortions in the data being modeled. We are proposing to combine a powerful graph-based image representation adapted to this specific task and frequent approximate subgraph (FAS) mining algorithms in order to classify images. In the case of image representation we are proposing to use more robust descriptors than our previous approach in this topic, and we also suggest a criterion to select the isomorphism threshold for the graph mining step. This proposal is tested in two well-known collections to show the improvement with respect to the previous related works.