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Support Vector Shape: A Classifier-Based Shape Representation

Nguyen, Hien Van; Porikli, Fatih


We introduce a novel implicit representation for 2D and 3D shapes based on Support Vector Machine (SVM) theory. Each shape is represented by an analytic decision function obtained by training SVM, with a Radial Basis Function (RBF) kernel so that the interior shape points are given higher values. This empowers support vector shape (SVS) with multifold advantages. First, the representation uses a sparse subset of feature points determined by the support vectors, which significantly improves the...[Show more]

CollectionsANU Research Publications
Date published: 2012
Type: Journal article
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI: 10.1109/TPAMI.2012.186


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