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Interactive image data labeling using self-organizing maps in an augmented reality scenario.

Holger Bekel1, Gunther Heidemann, Helge Ritter

  • 1Neuroinformatics Group, Bielefeld University, P.O. Box 10 01 31, D-33501 Bielefeld, Germany. hbekel@techfak.uni-bielefeld.de

Neural Networks : the Official Journal of the International Neural Network Society
|August 23, 2005
PubMed
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This study introduces a new method for labeling image patches from real-world environments using Augmented Reality (AR) gear. A Self-Organizing Map (SOM) helps users easily categorize visual data for improved object recognition.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Augmented Reality

Background:

  • Labeling image patches from unrestricted environments is challenging.
  • Mobile Augmented Reality (AR) systems require efficient methods for data acquisition and organization.
  • Existing methods lack convenience for user-driven training set composition.

Purpose of the Study:

  • To develop a convenient approach for labeling image patches captured in real-world scenarios.
  • To enable users to easily create training datasets for object recognition systems within AR.
  • To evaluate the effectiveness of the proposed system for image categorization.

Main Methods:

  • Utilizing head-mounted AR gear to sample 'interesting' image patches.
  • Training a Self-Organizing Map (SOM) on collected patches using MPEG-7 features.

Related Experiment Videos

  • Employing SOM for visualization and manual sorting of image patches into categories.

    • Main Results:

    • The system facilitates convenient manual sorting and categorization of image patches.
    • Satisfying categorization results were achieved with minimal user effort.
    • Demonstrated effectiveness using COIL-imagery and real-world office environment data.

    Conclusions:

    • The proposed approach simplifies the process of creating training datasets for AR systems.
    • The SOM-based method allows for efficient structuring and labeling of visual data.
    • This technique enhances the ability of AR systems to recognize unknown objects.