Gabor wavelet selection and SVM classification for object recognition

被引：2

作者：

Shen, Lin-Lin ^{[1
]}

Ji, Zhen ^{[1
]}

机构：

[1] School of Computer and Software Engineering, Shenzhen University

来源：

Zidonghua Xuebao/ Acta Automatica Sinica | 2009年 / 35卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Gabor feature; Object recognition; Support vector machine (SVM);

D O I：

10.1016/s1874-1029(08)60082-8

中图分类号：

学科分类号：

摘要：

This paper proposes a Gabor wavelets and support vector machine (SVM)-based framework for object recognition. When discriminative features are extracted at optimized locations using selected Gabor wavelets, classifications are done via SVM. Compared to conventional Gabor feature based object recognition system, the system developed in this paper is both robust and efficient. The proposed framework has been successfully applied to two object recognition applications, i. e., object/non-object classification and face recognition. Experimental results clearly show advantages of the proposed method over other approaches. © 2009 Acta Automatica Sinica. All rights reserved.

引用

页码：350 / 355

页数：5

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