Support Vector Shape: A Classifier-Based Shape Representation

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. Th...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2013-04, Vol.35 (4), p.970-982
Main Authors: Hien Van Nguyen, Porikli, F.
Format: Article
Language:English
Subjects:
2D and 3D representation
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Data processing. List processing. Character string processing
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Humans
Image Processing, Computer-Assisted - methods
Information, signal and communications theory
Kernel
Memory organisation. Data processing
Motion
Noise
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Robustness
Shape
Shape matching
Signal and communications theory
Signal, noise
Software
Support Vector Machine
Support vector machines
Telecommunications and information theory
Training
Vectors
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Summary: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 discriminative power against noise, fragmentation, and other artifacts that often come with the data. Second, the use of the RBF kernel provides scale, rotation, and translation invariant features, and allows any shape to be represented accurately regardless of its complexity. Finally, the decision function can be used to select reliable feature points. These features are described using gradients computed from highly consistent decision functions instead from conventional edges. Our experiments demonstrate promising results.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2012.186