Learning an Efficient and Robust Graph Matching Procedure for Specific Object Recognition

We present a fast and robust graph matching approach for 2D specific object recognition in images. From a small number of training images, a model graph of the object to learn is automatically built. It contains its local keypoints as well as their spatial proximity relationships. Training is based on a selection of the most efficient subgraphs using the mutual information. The detection uses dynamic programming with a lattice and thus is very fast. Experiments demonstrate that the proposed method outperforms the specific object detectors of the state-of-the-art in realistic noise conditions. Keywords-specific object recognition; cascade; graph matching;

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Jerome Revaud, Guillaume Lavoue, Yasuo Ariki, Atilla Baskurt
• Keywords: Computer Vision, ICPR 2010, Object Recognition, Specific Object, Specific Object Recognition