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.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Marwan Torki and Ahmed Elgammal
• Keywords: CVPR 2010, Graph Embeddings, Matching, Multi-set