A two-step approach to describing web topics via probable keywords and prototype images from background-removed similarities
文献类型：期刊论文
| 作者 | Pang, Junbiao1; Tao, Fei5; Li, Liang4; Huang, Qingming4,5; Yin, Baocai1,3; Tian, Qi2 |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2018-01-31 |
| 卷号 | 275页码:478-487 |
| 关键词 | Topic description Poisson deconvolution User-Generated Content Topic detection Background similarity Multi-modal description |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2017.08.057 |
| 英文摘要 | To quickly grasp what interesting topics are happening on web, it is challenge to discover and describe topics from User-Generated Content (UGC) data. Describing topics by probable keywords and prototype images is an efficient human-machine interaction to help person quickly grasp a topic. However, except for the challenges from web topic detection, mining the multi-media description is a challenge task that the conventional approaches can barely handle: (1) noises from non-informative short texts or images due to less-constrained UGC; and (2) even for these informative images, the gaps between visual concepts and social ones. This paper addresses above challenges from the perspective of background similarity remove, and proposes a two-step approach to mining the multi-media description from noisy data. First, we utilize a devcovolution model to strip the similarities among non-informative words/images during web topic detection. Second, the background-removed similarities are reconstructed to identify the probable keywords and prototype images during topic description. By removing background similarities, we can generate coherent and informative multi-media description for a topic. Experiments show that the proposed method produces a high quality description on two public datasets. (C) 2017 Elsevier B.V. All rights reserved. |
| 资助项目 | Natural Science Foundation of China[61332016] ; Natural Science Foundation of China[61672069] ; Natural Science Foundation of China[61472387] ; Natural Science Foundation of China[61620106009] ; Natural Science Foundation of China[U1636214] ; Natural Science Foundation of China[61429201] ; Natural Science Foundation of China[61650202] ; Beijing Post-Doctoral Research Foundation ; Beijing Municipal Commission of Education[KM201610005034] ; Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (PHR) ; ARO[W911NF-15-1-0290] ; NEC Laboratory of Blippar ; NEC Laboratory of America |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000418370200047 |
| 出版者 | ELSEVIER SCIENCE BV |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/6282]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Li, Liang |
| 作者单位 | 1.Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, 100 Pingleyuan Rd, Beijing 100124, Peoples R China 2.Univ Texas San Antonio, Dept Comp Sci, One UTSA Circle, San Antonio, TX 78249 USA 3.Dalian Univ Technol, 2 Linggong Rd, Dalian 116024, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Comp & Control Engn, 19 Yuquan Rd, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pang, Junbiao,Tao, Fei,Li, Liang,et al. A two-step approach to describing web topics via probable keywords and prototype images from background-removed similarities[J]. NEUROCOMPUTING,2018,275:478-487. |
| APA | Pang, Junbiao,Tao, Fei,Li, Liang,Huang, Qingming,Yin, Baocai,&Tian, Qi.(2018).A two-step approach to describing web topics via probable keywords and prototype images from background-removed similarities.NEUROCOMPUTING,275,478-487. |
| MLA | Pang, Junbiao,et al."A two-step approach to describing web topics via probable keywords and prototype images from background-removed similarities".NEUROCOMPUTING 275(2018):478-487. |
入库方式： OAI收割
来源：计算技术研究所
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