Classification of Endoscopic Images using Support Vector Machines

被引：1

作者：

Surangsrirat, Decho ^{[1
]}

Tapia, Moiez A. ^{[1
]}

Zhao, Weizhao ^{[2
]}

机构：

[1] Univ Miami, Dept Elect & Comp Engn, Coral Gables, FL 33124 USA

[2] Univ Miami, Dept Biomed Engn, Coral Gables, FL 33124 USA

来源：

IEEE SOUTHEASTCON 2010: ENERGIZING OUR FUTURE | 2010年

关键词：

UNSUPERVISED TEXTURE SEGMENTATION;

D O I：

10.1109/SECON.2010.5453834

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：436 / 439

页数：4

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