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

文献类型：期刊论文

作者Hu, Weiming2; Gao, Jun2; Li, Bing2; Wu, Ou3; Du, Junping1; Maybank, Stephen4
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2020-02-01
卷号32期号:2页码:218-233
ISSN号1041-4347
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
DOI10.1109/TKDE.2018.2882404
通讯作者Hu, Weiming(wmhu@nlpr.ia.ac.cn)
英文摘要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.
WOS关键词SALIENT OBJECT DETECTION ; OUTLIER DETECTION ; MODEL ; ALGORITHMS
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
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:000507883700002
资助机构Beijing Natural Science Foundation ; Natural Science Foundation of China ; NSFC ; Key Research Program of Frontier Sciences, CAS ; CAS External cooperation key project
源URL[http://ir.ia.ac.cn/handle/173211/29495]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
通讯作者Hu, Weiming
作者单位1.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China
2.Univ Chinese Acad Sci, Natl Lab Pattern Recognit Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
3.Tianjin Univ, Ctr Appl Math, Tianjin 300073, Peoples R China
4.Birkbeck Coll, Dept Comp Sci & Informat Syst, Malet St, London WC1E 7HX, England
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.

入库方式： OAI收割

来源：自动化研究所

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