Learning Large Scale Class Specific Hyper Graphs for Object Recognition

This paper describes how to construct a hyper-graph model from a large corpus of multi-view images using local invariant features. We commence by representing each image with a graph, which is constructed from a group of selected SIFT features. We then propose a new pairwise clustering method based on a graph matching similarity measure. The positive example graphs of a specific class accompanied with a set of negative example graphs are clustered into one or more clusters, which minimize an entropy function with a restriction defined on the F-measure. Each cluster is simplified into a tree structure composed of a series of irreducible graphs, and for each of which a node cooccurrence probability matrix is obtained. Finally, a recognition oriented class specific hyper-graph (CSHG) is generated from the given graph set. Experiments are performed on over 50 K training images spanning 500 objects and over 20 K test images of 68 objects. This demonstrates the scalability and recognition performance of our model.