Structural representation and BPTS learning for shape classification

Zhiyong Wang, Zheru Chi, D. Feng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

In this paper, a novel shape classification technique based on a hierarchical shape representation and the back-propagation through structure (BPTS) learning algorithm is proposed. In our representation scheme, a shape is hierarchically represented with the segments composing the contour of the shape by using a scale-space filtering method. The BPTS algorithm is then applied to learn to classify shapes with such a tree-structure representation. Simulations on both artificially generated shape patterns and real world gesture patterns show that robust classification results can be achieved by using a small set of features only.
Original languageEnglish
Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publicationComputational Intelligence for the E-Age
PublisherIEEE
Pages134-138
Number of pages5
Volume1
ISBN (Electronic)9789810475246, 9810475241
DOIs
Publication statusPublished - 1 Jan 2002
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Orchid Country Club, Singapore, Singapore
Duration: 18 Nov 200222 Nov 2002

Conference

Conference9th International Conference on Neural Information Processing, ICONIP 2002
CountrySingapore
CitySingapore
Period18/11/0222/11/02

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this