Object Recognition as Many-to-Many Feature Matching

Object recognition can be formulated as matching image features to model features. When recognition is exemplar-based, feature correspondence is one-to-one. However, segmentation errors, articulation, scale difference, and within-class deformation can yield image and model features which don't match one-to-one but rather many-tomany. Adopting a graph-based representation of a set of features, we present a matching algorithm that establishes many-to-many correspondences between the nodes of two noisy, vertex-labeled weighted graphs. Our approach reduces the problem of many-to-many matching of weighted graphs to that of many-to-many matching of weighted point sets in a normed vector space. This is accomplished by embedding the initial weighted graphs into a normed vector space with low distortion using a novel embedding technique based on a spherical encoding of graph structure. Many-to-many vector correspondences established by the Earth Mover's Distance framework are mapped b...

• Type: Journal
• Year: 2006
• Where: IJCV
• Authors: M. Fatih Demirci, Ali Shokoufandeh, Yakov Keselman, Lars Bretzner, Sven J. Dickinson
• Keywords: IJCV 2006, Many-to-many, Many-to-many Matching, Normed Vector Space