中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 多媒体计算与图形学团队
Deep-Structured Event Modeling for User-Generated Photos

文献类型：期刊论文

作者Yang, Xiaoshan1,2; Zhang, Tianzhu1,2; Xu, Changsheng1,2
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2018-08-01
卷号20期号:8页码:2100-2113
关键词Event analysis unusual event detection deep learning
ISSN号1520-9210
DOI10.1109/TMM.2017.2788210
通讯作者Yang, Xiaoshan(xiaoshan.yang@nlpr.ia.ac.cn)
英文摘要Vision-based event analysis is difficult because of the following challenges. The first challenge is intraclass variation. Photos uploaded by users are sparsely sampled visual appearances of an event over time. Thus, each photo may only capture a single object or scene of a specific complex event. The second challenge is interclass confusion. Photos related to different events may contain similar objects or scenes. Third, unusual events are characterized by scarcity, and only a few samples are available for use in learning event patterns. In this paper, by considering the photo timestamp, we propose a structured event modeling (SEM) framework for event analysis that exploits the temporal information of visual features and event classes in a photo sequence. Specifically, the temporal event patterns of the photo sequence and the relationships of different photos are jointly learned using deep neural networks (convolutional neural networks and recurrent neural networks) and a conditional random field. We evaluate the proposed SEM framework in two applications: multiclass event recognition and unusual event detection in photo sequences. The results of extensive experiments performed on a public event recognition dataset and a collected unusual event dataset demonstrate the effectiveness of the proposed method.
WOS关键词RECOGNITION ; COLLECTIONS ; MULTIMEDIA
资助项目National Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61572498] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[61702511] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[61711530243] ; Beijing Natural Science Foundation[4172062] ; Key Research Program of Frontier Sciences, Chinese Academy of Sciences[QYZDJ-SSW-JSC039]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000439378600015
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Key Research Program of Frontier Sciences, Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/26341]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Yang, Xiaoshan
作者单位1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng. Deep-Structured Event Modeling for User-Generated Photos[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2018,20(8):2100-2113.
APA Yang, Xiaoshan,Zhang, Tianzhu,&Xu, Changsheng.(2018).Deep-Structured Event Modeling for User-Generated Photos.IEEE TRANSACTIONS ON MULTIMEDIA,20(8),2100-2113.
MLA Yang, Xiaoshan,et al."Deep-Structured Event Modeling for User-Generated Photos".IEEE TRANSACTIONS ON MULTIMEDIA 20.8(2018):2100-2113.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。