You are here

Reinforcement Matching Using Region Context

TitleReinforcement Matching Using Region Context
Publication TypeConference Paper
Year of Publication2006
AuthorsDeng, H., E. N. Mortensen, L. G. Shapiro, and T. G. Dietterich
Conference Name2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06)
Pagination11 - 11
Date Published06/2006
Conference LocationNew York, NY

Local feature-based matching is robust to both clutter and occlusion. However, a primary shortcoming of local features is a deficiency of global information that can cause ambiguities in matching. Local features combined with global relationships convey much more information, but global spatial information is often not robust to occlusion and/or non-rigid transformations. This paper proposes a new framework for including global context information into local feature matching, while still maintaining robustness to occlusion, clutter, and nonrigid transformations. To generate global context information, we extend previous fixed-scale, circular-bin methods by using affine-invariant log-polar elliptical bins. Further, we employ a reinforcement matching scheme that provides greater robustness to occlusion and clutter than previous methods that non-discriminately compare accumulated bins values over the entire context. We also present a more robust method of calculating a feature's dominant orientation. We compare reinforcement matching to nearest neighbor matching without region context and to robust matching methods (RANSAC and PROSAC).