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 50K training images spanning～500 objects and over 20K test images of 68 objects. This demonstrates the scalability and recognition performance of our model.
Shengping Xia Edwin R. Hancock
ATR Lab, School of Electronic Science and Engineering, National University of Defense Technology, Ch Department of Computer Science, University of York, York YO10 5DD, UK
国际会议
The Fifth International Conference on Image and Graphics(第五届国际图像图形学学术会议 ICIG 2009)
西安
英文
366-371
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）