Classification of endoscopic images using support vector machines

Decho Surangsrirat, Moiez A. Tapia, Weizhao Zhao

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

3 Scopus citations

Abstract

This paper presents an application of support vector machines (SVMs) to multiclass problem in endoscopic image classification. Many studies have reported that SVMs have met with success in the texture classification problem. As an endoscopic image poses rich information expressed by texture features, we therefore investigate the potential of SVMs in this task. Strategy for multiclass problem based on an ensemble of binary classifiers is also implemented since the traditional SVMs algorithm deals with single label classification problems. The proposed scheme demonstrated an excellent classification result for multiclass problem in endoscopic image classification. We also show how a distortion correction helps further improve the results.

Original languageEnglish (US)
Title of host publicationIEEE SoutheastCon 2010
Subtitle of host publicationEnergizing Our Future
Pages436-439
Number of pages4
DOIs
StatePublished - May 31 2010
EventIEEE SoutheastCon 2010 Conference: Energizing Our Future - Charlotte-Concord, NC, United States
Duration: Mar 18 2010Mar 21 2010

Publication series

NameConference Proceedings - IEEE SOUTHEASTCON
ISSN (Print)0734-7502

Other

OtherIEEE SoutheastCon 2010 Conference: Energizing Our Future
CountryUnited States
CityCharlotte-Concord, NC
Period3/18/103/21/10

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Signal Processing

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