One-Shot Multi-Set Non-rigid Feature-Spatial Matching

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One-Shot Multi-Set Non-rigid Feature-Spatial Matching
We introduce a novel framework for nonrigid feature matching among multiple sets in a way that takes into consideration both the feature descriptor and the features spatial arrangement. We learn an embedded representation that combines both the descriptor similarity and the spatial arrangement in a unified Euclidean embedding space. This unified embedding is reached by minimizing an objective function that has two sources of weights; the feature spatial arrangement and the feature descriptor similarity scores across the different sets. The solution can be obtained directly by solving one Eigen-value problem that is linear in the number of features. Therefore, the framework is very efficient and can scale up to handle a large number of features. Experimental evaluation is done using different sets showing outstanding results compared to the state of the art; up to 100% accuracy is achieved in the case of the well known ‘Hotel’ sequence.
Marwan Torki and Ahmed Elgammal
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2010
Where CVPR
Authors Marwan Torki and Ahmed Elgammal
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