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Video events recognition by improved stochastic parsing based on extended stochastic context-free grammar representation
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作者 曹茂永 赵猛 +1 位作者 裴明涛 赵增顺 《Journal of Beijing Institute of Technology》 EI CAS 2013年第1期81-88,共8页
Video events recognition is a challenging task for high-level understanding of video se- quence. At present, there are two major limitations in existing methods for events recognition. One is that no algorithms are av... Video events recognition is a challenging task for high-level understanding of video se- quence. At present, there are two major limitations in existing methods for events recognition. One is that no algorithms are available to recognize events which happen alternately. The other is that the temporal relationship between atomic actions is not fully utilized. Aiming at these problems, an algo- rithm based on an extended stochastic context-free grammar (SCFG) representation is proposed for events recognition. Events are modeled by a series of atomic actions and represented by an extended SCFG. The extended SCFG can express the hierarchical structure of the events and the temporal re- lationship between the atomic actions. In comparison with previous work, the main contributions of this paper are as follows: ① Events (include alternating events) can be recognized by an improved stochastic parsing and shortest path finding algorithm. ② The algorithm can disambiguate the detec- tion results of atomic actions by event context. Experimental results show that the proposed algo- rithm can recognize events accurately and most atomic action detection errors can be corrected sim- ultaneously. 展开更多
关键词 video events recognition stochastic context-flee grammar stochastic parsing tempo-ral relationship
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Video Events Recognition by Scene and Group Context
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作者 裴明涛 王亚菲 赵猛 《China Communications》 SCIE CSCD 2013年第11期165-171,共7页
The ability to recognise video events has become increasingly more popular owing to its extensive practical applications.Most events will occur in certain scene with certain people,and the scene context and group cont... The ability to recognise video events has become increasingly more popular owing to its extensive practical applications.Most events will occur in certain scene with certain people,and the scene context and group context provide important information for event recognition.In this paper,we present an algorithm to recognise video events in different scenes in which there are multiple agents.First,we recognise events for each agent based on Stochastic Context Sensitive Grammar(SCSG).Then we propose the model of a scene in order to infer the scene in which the events occur,and we use a co-occurrence matrix of events to represent the group context.Finally,the scene and group context are exploited to distinguish events having similar structures.Experimental results show that by adding the scene and group context,the performance of events recognition can be significantly improved. 展开更多
关键词 events recognition scene context group context
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Event Type Recognition Based on Trigger Expansion 被引量:7
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作者 秦兵 赵妍妍 +2 位作者 丁效 刘挺 翟国富 《Tsinghua Science and Technology》 SCIE EI CAS 2010年第3期251-258,共8页
Event extraction is an important research point in information extraction, which includes two important sub-tasks of event type recognition and event argument recognition. This paper describes a method based on automa... Event extraction is an important research point in information extraction, which includes two important sub-tasks of event type recognition and event argument recognition. This paper describes a method based on automatic expansion of the event triggers for event type recognition. The event triggers are first extended through a thesaurus to enable the extraction of the candidate events and their candidate types. Then, a binary classification method is used to recognize the candidate event types. This method effectively improves the unbalanced data problem in training models and the data sparseness problem with a small corpus. Evaluations on the ACE2005 dataset give a final F-score of 61.24%, which outperforms traditional methods based on pure machine learning. 展开更多
关键词 event extraction event type recognition event trigger
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