Learning Patterns from Images by Combining Soft Decisions and Hard Decisions

We present a novel approach for learning patterns (sub-images) shared by multiple images without prior knowledge about the number and the positions of the patterns in the images. The patterns may undergo kinds of rigid and non-rigid transformations. To reduce the searching space, the images are pre-segmented and represented by attribute relation graphs (ARGs). The problem is then formulated as learning the isomorphic subgraph, called pattern ARG (PARG), from multiple sample ARGs (SARG) with regard to the attribute similarity and the relation similarity. An inexact graphmatching algorithm is proposed to establish the correspondence between each SARG and the PARG. Inexact graph matching and model editing based on Bayes’ decision rule are incorporated into Generalized Expectation and Maximization (GEM) algorithm. The modified GEM algorithm combines soft decisions and hard decisions together to learn both the appearance and the structure of the PARG. In the experiments, the learned PARG successfully captures the appearance and spatial information of the concept shared by the images.