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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
作者部门光学影像学习与分析中心
2019-03
发表期刊IET COMPUTER VISION
ISSN1751-9632;1751-9640
卷号13期号:2(SI)页码:178-187
产权排序5
摘要

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
收录类别SCI
语种英语
WOS记录号WOS:000459454900013
出版者INST ENGINEERING TECHNOLOGY-IET
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/31166
专题光学影像学习与分析中心
通讯作者Huang, Qinghua
作者单位1.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
推荐引用方式
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.
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