Image Classification using Wavelet Coefficients for Video Surveillance

被引：0

作者：

Fai, Ong Yew ^{[1
]}

Voon, Yap Vooi ^{[1
]}

Nisar, Humaira ^{[1
]}

机构：

[1] Univ Tunku Abdul Rahman UTAR, FEGT, Kampar, Perak, Malaysia

来源：

2016 IEEE CONFERENCE ON SYSTEMS, PROCESS AND CONTROL (ICSPC) | 2016年

关键词：

wavelet transform; feature extraction; image classification; video surveillance;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper a new approach of wavelet coefficient features to identify and classify different images encountered by video surveillance systems is introduced. The main objective is to identify specific wavelet filters for achieving a better classification of images. The fundamental properties of wavelet coefficients for feature selection for image classification are investigated. Haar, Daubechies 5, Symlet 2, and Biorthogonal 2.2 wavelets have been used in this investigation. The results show that Haar wavelet provides promising results in object retrieval compared to Daubechies 5, Symlet 2, and Biorthogonal 2.2 wavelets.

引用

页码：107 / 112

页数：6

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