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Support vector shape: a classifier-based shape representation.

Hien Van Nguyen1, Fatih Porikli

  • 1University of Maryland, College Park, MD 20740, USA. hien@umd.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|February 23, 2013
PubMed
Summary
This summary is machine-generated.

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We developed a new shape representation using Support Vector Machines (SVM) and Radial Basis Function (RBF) kernels. This Support Vector Shape (SVS) method enhances accuracy and robustness for 2D and 3D shape analysis.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Computational Geometry

Background:

  • Traditional shape representations often struggle with noise and data fragmentation.
  • Existing methods may lack robustness to transformations like scaling and rotation.
  • Accurate and invariant shape representation is crucial for many computer vision tasks.

Purpose of the Study:

  • To introduce a novel implicit shape representation using Support Vector Machine (SVM) theory.
  • To leverage SVM with a Radial Basis Function (RBF) kernel for robust shape analysis.
  • To demonstrate the advantages of the Support Vector Shape (SVS) representation.

Main Methods:

  • Representing shapes via an analytic decision function trained using SVM with an RBF kernel.
  • Assigning higher values to interior shape points to define the shape implicitly.

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  • Utilizing support vectors to derive a sparse set of discriminative feature points.

    • Main Results:

    • The Support Vector Shape (SVS) representation exhibits improved discriminative power against noise and artifacts.
    • RBF kernel ensures scale, rotation, and translation invariant features for accurate shape representation.
    • Gradients from consistent decision functions provide reliable feature points, outperforming conventional edges.

    Conclusions:

    • The proposed Support Vector Shape (SVS) offers a robust and accurate implicit shape representation.
    • This method effectively handles complex shapes and noisy data.
    • The approach shows promising results for advanced shape analysis and recognition.