Image feature extraction algorithm for support vector machines using multi-layer block model

Wonjun Hwang, Hanseok Ko

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper concerns recognizing 3-dimensional object using proposed multi-layer block model. In particular, we aim to achieve desirable recognition performance while restricting the computational load to a low level using 3-step feature extraction procedure. An input image is first precisely partitioned into hierarchical layers of blocks in the form of base blocks and overlapping blocks. The hierarchical blocks are merged into a matrix, with which abundant local feature information can be obtained. The local features extracted are then employed by the kernel based support vector machines in tournament for enhanced system recognition performance while keeping it to low dimensional feature space. The simulation results show that the proposed feature extraction method reduces the computational load by over 80% and preserves the stable recognition rate from varying illumination and noise conditions.

Original languageEnglish
Pages (from-to)623-632
Number of pages10
JournalIEICE Transactions on Information and Systems
VolumeE86-D
Issue number3
Publication statusPublished - 2003 Mar 1

Fingerprint

Support vector machines
Feature extraction
Lighting

Keywords

  • Computational load
  • Dimension reduction
  • Feature extraction
  • Object recognition
  • Vehicle recognition

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems
  • Software

Cite this

Image feature extraction algorithm for support vector machines using multi-layer block model. / Hwang, Wonjun; Ko, Hanseok.

In: IEICE Transactions on Information and Systems, Vol. E86-D, No. 3, 01.03.2003, p. 623-632.

Research output: Contribution to journalArticle

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