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ICCV
2009
IEEE

Feature Correspondence and Deformable Object Matching via Agglomerative Correspondence Clustering

9 years 11 months ago
Feature Correspondence and Deformable Object Matching via Agglomerative Correspondence Clustering
We present an efficient method for feature correspondence and object-based image matching, which exploits both photometric similarity and pairwise geometric consistency from local invariant features. We formulate objectbased image matching as an unsupervised multi-class clustering problem on a set of candidate feature matches, and propose a novel pairwise dissimilarity measure and a robust linkage model in the framework of hierarchical agglomerative clustering. The algorithm handles significant amount of outliers and deformation as well as multiple clusters, thus enabling simultaneous feature matching and clustering from real-world image pairs with significant clutter and multiple deformable objects. The experimental evaluation on feature correspondence, object recognition, and object-based image matching demonstrates that our method is robust to both outliers and deformation, and applicable to a wide range of image matching problems.
Minsu Cho (Seoul National University), Jungmin Lee
Added 19 Jul 2009
Updated 17 May 2010
Type Conference
Year 2009
Where ICCV
Authors Minsu Cho (Seoul National University), Jungmin Lee (Seoul National University), Kyoung Mu Lee (Seoul National University)
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