OPT OpenIR  > 光学影像学习与分析中心
Co-occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition
Yuan, Feiniu1,2; Shi, Jinting3; Xia, Xue2; Huang, Qinghua4,5; Li, Xuelong6
Department光学影像学习与分析中心
2019-03
Source PublicationIET COMPUTER VISION
ISSN1751-9632;1751-9640
Volume13Issue:2(SI)Pages:178-187
Contribution Rank5
Abstract

It is challenging to recognize smoke from visual scenes due to large variations of smoke colors, textures and shapes. To improve robustness, we propose a novel feature extraction method based on similarity and dissimilarity matching measures of Local Binary Patterns (LBP). Given two bit-sequences of an LBP code pair, the similarity and dissimilarity matching measures are defined as the ratios of the 1-1 bitwise matching number to the 0-0 bitwise matching number and the 1-0 number to the 0-1 number, respectively. To capture local code variations, we calculate the measures between LBP codes of a center pixel and its neighbors. Then we compare each measure with its global mean to propose Similarity Matching based Local Binary Patterns (SMLBP) and Dissimilarity Matching based Local Binary Patterns (DMLBP). Since SMLBP and DMLBP extract spatial variations of the 1st order LBP codes, they actually represent the 2nd order variations of pixel values. Furthermore, we adopt different mapping modes and multi-scale neighborhoods to obtain rotation and scale invariances. Finally, we concatenate the histograms of LBP, SMLBP and DMLBP to generate a feature vector containing 1st and 2nd order information. Experiments show that our method obviously outperforms existing methods.

DOI10.1049/iet-cvi.2018.5164
Indexed BySCI ; EI
Language英语
WOS IDWOS:000459454900013
PublisherINST ENGINEERING TECHNOLOGY-IET
EI Accession Number20190906559203
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31166
Collection光学影像学习与分析中心
Corresponding AuthorHuang, Qinghua
Affiliation1.Shanghai Normal Univ, Coll Informat Mech & Elect Engn, Shanghai 201418, Peoples R China
2.Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang 330032, Jiangxi, Peoples R China
3.Jiangxi Agr Univ, Vocat Sch Teachers & Technol, Nanchang 330045, Jiangxi, Peoples R China
4.Northwestern Polytech Univ, Sch Mech Engn, Xian 710072, Shaanxi, Peoples R China
5.Northwestern Polytech Univ, Ctr Opt Magery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China
6.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Yuan, Feiniu,Shi, Jinting,Xia, Xue,et al. Co-occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition[J]. IET COMPUTER VISION,2019,13(2(SI)):178-187.
APA Yuan, Feiniu,Shi, Jinting,Xia, Xue,Huang, Qinghua,&Li, Xuelong.(2019).Co-occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition.IET COMPUTER VISION,13(2(SI)),178-187.
MLA Yuan, Feiniu,et al."Co-occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition".IET COMPUTER VISION 13.2(SI)(2019):178-187.
Files in This Item:
File Name/Size DocType Version Access License
Co-occurrence matchi(2509KB)期刊论文出版稿开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yuan, Feiniu]'s Articles
[Shi, Jinting]'s Articles
[Xia, Xue]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yuan, Feiniu]'s Articles
[Shi, Jinting]'s Articles
[Xia, Xue]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yuan, Feiniu]'s Articles
[Shi, Jinting]'s Articles
[Xia, Xue]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Co-occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.