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Shape indexing using self-organizing maps.

P N Suganthan1

  • 1Sch. of Electr. and Electron. Eng., Nanyang Technol. Univ., Singapore.

IEEE Transactions on Neural Networks
|February 5, 2008
PubMed
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This study introduces a new method for mapping shapes while preserving their topology using self-organizing maps (SOMs). The approach effectively represents shape structures, enabling invariant mapping under various transformations.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Computational Geometry

Background:

  • Representing structural information of geometric shapes is crucial for various applications.
  • Existing methods may struggle with preserving topological properties during shape transformation analysis.
  • Self-organizing maps (SOMs) offer a powerful tool for dimensionality reduction and feature extraction.

Purpose of the Study:

  • To propose a novel topology-preserving mapping methodology for structural shapes.
  • To leverage self-organizing maps (SOMs) for accurate shape representation and analysis.
  • To achieve invariance to common geometric transformations in shape mapping.

Main Methods:

  • Capturing structural information of geometrical shapes using relational attribute vectors.

Related Experiment Videos

  • Quantizing relational attribute vectors using a self-organizing map (SOM).
  • Generating shape histograms from the SOM and training a second SOM for topology-preserving mapping.

    • Main Results:

    • The proposed method successfully generates topology-preserving mappings of geometric shapes.
    • The methodology demonstrates invariance to transformations like rotation, translation, scale, affine, and perspective transformations.
    • Experimental results on trademark objects validate the effectiveness of the approach.

    Conclusions:

    • The novel SOM-based approach provides an effective way to map structural shapes while preserving topology.
    • The method offers robustness against various geometric transformations, enhancing its practical applicability.
    • This work contributes a valuable technique for shape analysis and representation in computer vision and machine learning.