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Subject: Automation & Control Systems ; Computer Science
Title:
extended probabilistic latent semantic analysis model for topics in time-stamped images
Author: Liao Xiaofeng ; Wang Yongji ; Ding Liping ; Gu Jian
Keyword: Extended Probabilistic Latent Semantic Analysis ; Temporal Image Mining
Source: INTELLIGENT AUTOMATION AND SOFT COMPUTING
Issued Date: 2011
Volume: 17, Issue:7, Pages:983-995
Indexed Type: SCI
Department: Liao Xiaofeng; Wang Yongji; Ding Liping Chinese Acad Sci Natl Engn Res Ctr Fundamental Software Inst Software Beijing Peoples R China. Liao Xiaofeng Nanchang Univ Informat Engn Sch Nanchang Peoples R China. Gu Jian Minist Publ Secur Res Inst 3 Key Lab Informat Network Secur Shanghai Peoples R China.
Sponsorship: State Key Laboratory of Independent ResearchCSZZ0808; Chinese Academy of Sciences Institute of SoftwareYOCX285056; Accessing-Verification-Protection oriented secure operating system prototypeKGCX2-YW-125; Key Lab of Information Network Security of Ministry of Public Security(The Third Research Institute of Ministry of Public Security)
Abstract: This paper considers the problem of modelling the topics in a sequence of images with known time stamp. Detecting and tracking of temporal data is an important task in multiple applications, such as finding hot research point from scientific literature, news article series analysis, email surveillance, search query log mining, etc. In contrast to existing works mainly focusing on text document collections, this paper considers mining temporal topic trends from image data set. An extension of the Probabilistic Latent Semantic Analysis(PLSA) model, which includes an additional variable associated with the time stamp to better model the temporal topics, is presented to extract topics among images and tract how topics change over time. Experiments show the effectiveness of this method.
English Abstract: This paper considers the problem of modelling the topics in a sequence of images with known time stamp. Detecting and tracking of temporal data is an important task in multiple applications, such as finding hot research point from scientific literature, news article series analysis, email surveillance, search query log mining, etc. In contrast to existing works mainly focusing on text document collections, this paper considers mining temporal topic trends from image data set. An extension of the Probabilistic Latent Semantic Analysis(PLSA) model, which includes an additional variable associated with the time stamp to better model the temporal topics, is presented to extract topics among images and tract how topics change over time. Experiments show the effectiveness of this method.
Language: 英语
WOS ID: WOS:000296754800014
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16090
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Liao Xiaofeng,Wang Yongji,Ding Liping,et al. extended probabilistic latent semantic analysis model for topics in time-stamped images[J]. INTELLIGENT AUTOMATION AND SOFT COMPUTING,2011-01-01,17(7):983-995.
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