Image classification using frequent approximate subgraphs

Abstract

Frequent approximate subgraph (FAS) mining is used in applications where it is important to take into account some tolerance under slight distortions in the data. Following this consideration, some FAS miners have been developed and applied in several domains of science. However, there are few works related to the application of these types of graph miners in classification tasks. In this paper, we propose a new framework for image classification, which uses FAS patterns as features. We also propose to compute automatically the substitution matrices needed in the process, instead of using expert knowledge. Our approach is tested in two real image collections showing that it obtains good results, comparable to other non-miner solutions reported, and that FAS mining is better than the exact approach for this task. © 2012 Springer-Verlag.