Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression

doi:10.1109/TKDE.2018.2882404

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 视频内容安全

	Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression
	Hu, Weiming1 ; Gao, Jun 1; Li, Bing1 ; Wu, Ou 2; Du, Junping 3; Maybank, Stephen 4
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
	2020-02-01
卷号	32 期号:2 页码:218-233
通讯作者	Hu, Weiming(wmhu@nlpr.ia.ac.cn)
摘要	Current local density-based anomaly detection methods are limited in that the local density estimation and the neighborhood density estimation are not accurate enough for complex and large databases, and the detection performance depends on the size parameter of the neighborhood. In this paper, we propose a new kernel function to estimate samples' local densities and propose a weighted neighborhood density estimation to increase the robustness to changes in the neighborhood size. We further propose a local kernel regression estimator and a hierarchical strategy for combining information from the multiple scale neighborhoods to refine anomaly factors of samples. We apply our general anomaly detection method to image saliency detection by regarding salient pixels in objects as anomalies to the background regions. Local density estimation in the visual feature space and kernel-based saliency score propagation in the image enable the assignment of similar saliency values to homogenous object regions. Experimental results on several benchmark datasets demonstrate that our anomaly detection methods overall outperform several state-of-art anomaly detection methods. The effectiveness of our image saliency detection method is validated by comparison with several state-of-art saliency detection methods.
关键词	Anomaly detection Kernel Estimation Saliency detection Visualization Data models Computational modeling Anomaly detection local kernel density estimation weighted neighborhood density hierarchical context-based local kernel regression
DOI	10.1109/TKDE.2018.2882404
关键词[WOS]	SALIENT OBJECT DETECTION ; OUTLIER DETECTION ; MODEL ; ALGORITHMS
收录类别	SCI
语种	英语
资助项目	Beijing Natural Science Foundation[L172051] ; Natural Science Foundation of China[61772083] ; Natural Science Foundation of China[61532006] ; Natural Science Foundation of China[61751212] ; Natural Science Foundation of China[61721004] ; NSFC[U1636218] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSWJSC040] ; CAS External Cooperation Key Project ; Beijing Natural Science Foundation[L172051] ; Natural Science Foundation of China[61772083] ; Natural Science Foundation of China[61532006] ; Natural Science Foundation of China[61751212] ; Natural Science Foundation of China[61721004] ; NSFC[U1636218] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSWJSC040] ; CAS External cooperation key project
项目资助者	Beijing Natural Science Foundation ; Natural Science Foundation of China ; NSFC ; Key Research Program of Frontier Sciences, CAS ; CAS External cooperation key project
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000507883700002
出版者	IEEE COMPUTER SOC
七大方向——子方向分类	目标检测、跟踪与识别
引用统计	被引频次：54[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/29496
专题	多模态人工智能系统全国重点实验室_视频内容安全
通讯作者	Hu, Weiming
作者单位	1.Univ Chinese Acad Sci, Natl Lab Pattern Recognit Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 2.Tianjin Univ, Ctr Appl Math, Tianjin 300073, Peoples R China 3.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China 4.Birkbeck Coll, Dept Comp Sci & Informat Syst, Malet St, London WC1E 7HX, England
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Hu, Weiming,Gao, Jun,Li, Bing,et al. Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2020,32(2):218-233.
APA	Hu, Weiming,Gao, Jun,Li, Bing,Wu, Ou,Du, Junping,&Maybank, Stephen.(2020).Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,32(2),218-233.
MLA	Hu, Weiming,et al."Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 32.2(2020):218-233.