discovering hot topics from geo-tagged video
文献类型：期刊论文
| 作者 | Liu Kuien ; Xu Jiajie ; Zhang Longfei ; Ding Zhiming ; Li Mingshu |
| 刊名 | Neurocomputing
 |
| 出版日期 | 2012 |
| 卷号 | 105页码:- |
| 关键词 | Heuristic methods Information management Signal processing Video recording Websites |
| ISSN号 | 0925-2312 |
| 中文摘要 | As video data generated by users boom continuously, making sense of large scale data archives is considered as a critical challenge for data management. Most existing learning techniques that extract signal-level contents from video data struggle to scale due to efficiency limits. With the development of pervasive positioning techniques, discovering hot topics from multimedia data by their geographical tags has become practical: videos taken by advanced cameras are associated with GPS locations, and geo-tagged videos from YouTube can be identified by their associated GPS locations on Google Maps. It enables us to know the cultures, scenes, and human behaviors from videos based on their spatio-temporal distributions. However, meaningful topic discovery requires an efficient clustering approach, through which coherent topics can be detected according to particular geographical regions without out-of-focus effects. To handle this problem, this paper presents a filter-refinement framework to discover hot topics corresponding to geographical dense regions, and then introduces two novel metrics to refine unbounded hot regions, together with a heuristic method for setting rational thresholds on these metrics. The results of extensive experiments prove that hot topics can be efficiently discovered by our framework, and more compact topics can be achieved after using the novel metrics. © 2012 Elsevier B.V. All rights reserved. |
| 英文摘要 | As video data generated by users boom continuously, making sense of large scale data archives is considered as a critical challenge for data management. Most existing learning techniques that extract signal-level contents from video data struggle to scale due to efficiency limits. With the development of pervasive positioning techniques, discovering hot topics from multimedia data by their geographical tags has become practical: videos taken by advanced cameras are associated with GPS locations, and geo-tagged videos from YouTube can be identified by their associated GPS locations on Google Maps. It enables us to know the cultures, scenes, and human behaviors from videos based on their spatio-temporal distributions. However, meaningful topic discovery requires an efficient clustering approach, through which coherent topics can be detected according to particular geographical regions without out-of-focus effects. To handle this problem, this paper presents a filter-refinement framework to discover hot topics corresponding to geographical dense regions, and then introduces two novel metrics to refine unbounded hot regions, together with a heuristic method for setting rational thresholds on these metrics. The results of extensive experiments prove that hot topics can be efficiently discovered by our framework, and more compact topics can be achieved after using the novel metrics. © 2012 Elsevier B.V. All rights reserved. |
| 收录类别 | EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000317091700012 |
| 公开日期 | 2013-09-17 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/15149]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 GB/T 7714 | Liu Kuien,Xu Jiajie,Zhang Longfei,et al. discovering hot topics from geo-tagged video[J]. Neurocomputing,2012,105:-. |
| APA | Liu Kuien,Xu Jiajie,Zhang Longfei,Ding Zhiming,&Li Mingshu.(2012).discovering hot topics from geo-tagged video.Neurocomputing,105,-. |
| MLA | Liu Kuien,et al."discovering hot topics from geo-tagged video".Neurocomputing 105(2012):-. |
入库方式： OAI收割
来源：软件研究所
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