Structural representation and BPTS learning for shape classification

Zhiyong Wang; Zheru Chi; D. Feng

doi:10.1109/ICONIP.2002.1202146

Structural representation and BPTS learning for shape classification

Zhiyong Wang, Zheru Chi, D. Feng

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-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 language	English
Title of host publication	ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publication	Computational Intelligence for the E-Age
Publisher	IEEE
Pages	134-138
Number of pages	5
Volume	1
ISBN (Electronic)	9789810475246, 9810475241
DOIs	https://doi.org/10.1109/ICONIP.2002.1202146
Publication status	Published - 1 Jan 2002
Event	9th International Conference on Neural Information Processing, ICONIP 2002 - Orchid Country Club, Singapore, Singapore Duration: 18 Nov 2002 → 22 Nov 2002

Conference

Conference	9th International Conference on Neural Information Processing, ICONIP 2002
Country/Territory	Singapore
City	Singapore
Period	18/11/02 → 22/11/02

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

More information

10.1109/ICONIP.2002.1202146

Cite this

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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.",

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Wang, Z, Chi, Z & Feng, D 2002, Structural representation and BPTS learning for shape classification. in ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. vol. 1, 1202146, IEEE, pp. 134-138, 9th International Conference on Neural Information Processing, ICONIP 2002, Singapore, Singapore, 18/11/02. https://doi.org/10.1109/ICONIP.2002.1202146

Structural representation and BPTS learning for shape classification. / Wang, Zhiyong; Chi, Zheru; Feng, D.

ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. Vol. 1 IEEE, 2002. p. 134-138 1202146.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - 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.

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Structural representation and BPTS learning for shape classification

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