Manifold learning based on multi-feature for road-sign recognition

Qieshi Zhang, Sei Ichiro Kamata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, a multi-feature selection and application based manifold learning metric method is proposed for Road-Sign Recognition (RSR). Firstly, the manifold metric between manifold from subspace is discussed in detail. After that, the multi-feature analyzing, selection, classification and application are introduced for rough recognition and create the manifold. Then the proposed method is used to evaluate the distance between the manifolds. Finally, the RSR results suggest that the proposed method is robust than other methods.

Original languageEnglish
Title of host publicationSICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts
PublisherSociety of Instrument and Control Engineers (SICE)
Pages1143-1146
Number of pages4
ISBN (Print)9784907764395
Publication statusPublished - 2011 Jan 1
Event50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo, Japan
Duration: 2011 Sep 132011 Sep 18

Publication series

NameProceedings of the SICE Annual Conference

Other

Other50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011
CountryJapan
CityTokyo
Period11/9/1311/9/18

Keywords

  • Feature Analyzing
  • Feature Selection
  • Manifold Learning
  • Road-Sign Recognition (RSR)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Zhang, Q., & Kamata, S. I. (2011). Manifold learning based on multi-feature for road-sign recognition. In SICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts (pp. 1143-1146). [6060505] (Proceedings of the SICE Annual Conference). Society of Instrument and Control Engineers (SICE).