Shape classification through structured learning of matching measures
Many traditional methods for shape classification involve establishing point correspondences between shapes to produce matching scores, which are in turn used as similarity measures for classification. Learning techniques have been applied only in the second stage of this process, after the matching scores have been obtained. In this paper, instead of simply taking for granted the scores obtained by matching and then learning a classifier, we learn the matching scores themselves so as to...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceeings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009)|
|01_Chen_Shape_classification_through_2009.pdf||1.75 MB||Adobe PDF||Request a copy|
|02_Chen_Shape_classification_through_2009.pdf||46.19 kB||Adobe PDF||Request a copy|
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