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A hierarchical classifier using new support vector machines for automatic target recognition.

David Casasent1, Yu-Chiang Wang

  • 1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA. casasent@ece.cmu.edu

Neural Networks : the Official Journal of the International Neural Network Society
|August 10, 2005
PubMed
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A novel hierarchical classifier effectively identifies targets and rejects unknown non-target inputs. This approach shows excellent initial results for infra-red data analysis.

Area of Science:

  • Computer Science
  • Signal Processing
  • Machine Learning

Background:

  • Automatic target recognition (ATR) systems require robust classification of objects.
  • Handling unseen non-target inputs presents a significant challenge in ATR.
  • Hierarchical classification offers a structured approach to complex recognition tasks.

Purpose of the Study:

  • To propose and evaluate a binary hierarchical classifier for automatic target recognition.
  • To address the challenge of rejecting unknown non-target inputs during classification.
  • To leverage the generalization and rejection capabilities of Support Vector Representation and Discrimination Machines (SVRDM).

Main Methods:

  • Implementation of a binary hierarchical classifier.
  • Utilization of Support Vector Representation and Discrimination Machine (SVRDM) at each hierarchical node.

Related Experiment Videos

  • Employing magnitude Fourier Transform (|FT|) features for shift-invariance.
  • Testing on infra-red (IR) data.

    • Main Results:

    • The hierarchical SVRDM classifier demonstrated strong performance.
    • Effective rejection of non-target inputs was achieved.
    • Initial tests on infra-red data yielded excellent results.

    Conclusions:

    • The proposed hierarchical SVRDM classifier is a promising approach for automatic target recognition.
    • The method shows efficacy in handling unseen data and providing robust rejection.
    • Magnitude Fourier Transform features contribute positively to the classifier's performance in IR data analysis.