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Fast Contour Matching Using Approximate Earth Mover's Distance

dc.date.accessioned2004-10-08T20:43:06Z
dc.date.accessioned2018-11-24T10:21:42Z
dc.date.available2004-10-08T20:43:06Z
dc.date.available2018-11-24T10:21:42Z
dc.date.issued2003-12-05en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6733
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/6733
dc.description.abstractWeighted graph matching is a good way to align a pair of shapes represented by a set of descriptive local features; the set of correspondences produced by the minimum cost of matching features from one shape to the features of the other often reveals how similar the two shapes are. However, due to the complexity of computing the exact minimum cost matching, previous algorithms could only run efficiently when using a limited number of features per shape, and could not scale to perform retrievals from large databases. We present a contour matching algorithm that quickly computes the minimum weight matching between sets of descriptive local features using a recently introduced low-distortion embedding of the Earth Mover's Distance (EMD) into a normed space. Given a novel embedded contour, the nearest neighbors in a database of embedded contours are retrieved in sublinear time via approximate nearest neighbors search. We demonstrate our shape matching method on databases of 10,000 images of human figures and 60,000 images of handwritten digits.en_US
dc.format.extent16 p.en_US
dc.format.extent7561935 bytes
dc.format.extent7530316 bytes
dc.language.isoen_US
dc.subjectAIen_US
dc.subjectcontour matchingen_US
dc.subjectshape matchingen_US
dc.subjectEMDen_US
dc.subjectimage retrievalen_US
dc.titleFast Contour Matching Using Approximate Earth Mover's Distanceen_US


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