[1]严云洋,陈垂雄,刘以安,等.维度加权模式动态纹理特征的火焰检测[J].智能系统学报,2017,(04):548-555.[doi:10.11992/tis.201607021]
 YAN Yunyang,CHEN Chuixiong,LIU Yian,et al.Fire detection based on dynamic texture featuresunder a dimension-weighted mode[J].CAAI Transactions on Intelligent Systems,2017,(04):548-555.[doi:10.11992/tis.201607021]
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维度加权模式动态纹理特征的火焰检测(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
期数:
2017年04期
页码:
548-555
栏目:
出版日期:
2017-08-25

文章信息/Info

Title:
Fire detection based on dynamic texture featuresunder a dimension-weighted mode
作者:
严云洋12 陈垂雄12 刘以安2 高尚兵1
1. 淮阴工学院 计算机与软件工程学院, 江苏 淮安 223003;
2. 江南大学 物联网工程学院, 江苏 无锡 214122
Author(s):
YAN Yunyang12 CHEN Chuixiong12 LIU Yi’an2 GAO Shangbing1
1. Faculty of Computer & Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China;
2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
关键词:
静态纹理动态纹理正交特征加权特征支持向量机火焰检测特征提取局部二值模式
Keywords:
static texturedynamic textureorthogonal featureweighted featuresupport vector machineflame detectionfeature extractionlocal binary pattern
分类号:
TP391
DOI:
10.11992/tis.201607021
摘要:
对疑似火焰区域提取纹理特征时,用局部三值模式描述火焰静态纹理特征不利于区分火焰与其他纹理均匀的干扰物,用KNN算法(k-nearest neighbor algorithm)分类效率较低。针对这些问题,提出用三正交平面局部混合模式(three orthogonal planes local mixed pattern,LMP-TOP)描述火焰的静动态纹理,再输入维度加权的支持向量机进行分类识别。LMP-TOP是对第一维XY平面,采用八邻域的均匀局部二值模式(uniform local binary pattern,LBPu2)三正交平面局部混合模式表示火焰的静态纹理特征;对第二维XT和第三维YT平面,则采用局部三值模式(local ternary patter,LTP)融入火焰在时间维度上的变化信息,这样在得到火焰的静态特征的同时也融入了其动态特征。根据3个维度单独用于识别的准确率,赋予其相应的权重,用维度加权的支持向量机进行分类识别。实验结果表明,相比Sthevanie等算法,本文所提出的方法火焰识别率和检测效率均较高。
Abstract:
In fire detection modeling, a local ternary pattern is generally used to extract the static and dynamic textures of the suspected flame. But it is difficult to distinguish the flame from other uniform texture interferences when a local ternary pattern is used to describe the static texture features. The efficiency is low when the KNN (k-Nearest Neighbor) algorithm is used for classification. Aimed at solving these problems, a novel method is proposed here, whereby an LMP-TOP (local mixed pattern-three orthogonal planes) method is used to depict the static and dynamic textures of a suspected flame area. A dimension-weighted support vector machine was used for the classification. Applying LMP-TOP, an eight neighborhood uniform local binary pattern (LBPu2) was used to denote the static texture features of the flame on the 1st-dimension plane XY, and a local ternary pattern was used to describe the change in flame information on the 2nd-and 3rd -dimension planes, XT and YT respectively, by fusing with information in the time dimension. The static and dynamic characteristics of the flame were therefore integrated. The dimension weight was assigned according to the individual recognition accuracy. Then, a support vector machine with dimension weighting was used for classification. Experimental results show that the accuracy of flame identification and the detection efficiency are better with the proposed method than with corresponding algorithms such as Sthevanie.

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备注/Memo

备注/Memo:
收稿日期:2016-07-22。
基金项目:国家自然科学基金项目(61402192);江苏省"六大人才高峰"项目(2013DZXX-023);江苏省"333工程"(BRA2013208);淮安市科技计划项目(HAG2013057,HAG2013059).
作者简介:严云洋,男,1967年生,教授、博士、CCF会员,江苏省计算机学会常务理事及人工智能专委会副主任委员,主要研究方向为数字图像处理、模式识别,发表学术论文100余篇,其中被SCI、EI检索50余篇;陈垂雄,男,1988年生,硕士研究生,主要研究方向为数字图像处理、模式识别;刘以安,男,1963年生,博士、教授,主要研究方向为模式识别、数据融合。
通讯作者:严云洋,E-mail:areyyyke@163.com.
更新日期/Last Update: 2017-08-25