A framework for key-frame selection based on relevance feedback

ISCAS OpenIR

	A framework for key-frame selection based on relevance feedback
	Ma, Cuixia (1); Wen, Ruri (1); Zeng, Dejun (1); Wang, Hongan (1); Dai, Guozhong (1)
	2013
会议名称	12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry, VRCAI 2013
页码	259-264
会议日期	November 17, 2013 - November 19, 2013
会议地点	Hong Kong, Hong kong
收录类别	EI
出版地	Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States
ISBN	9781450325905
部门归属	(1) Institute of Software, Chinese Academy of Sciences, Beijing, China
摘要	With the explosive growth of video resource, efficient techniques for generating video summarization are appealing for facilitating understanding and presenting video content. Traditional video summarizations were usually given through extracting key frames based on the features of frames in video sequence. However, in many cases, the given key frames don't meet the key frames reside in the mind of users. In this paper, we propose an innovative approach based on relevance feedback to select the key frames of a video sequence for video summarization considering users' subjective visual preference. A two-step strategy to select the key frames is given. 1) we evaluate the preference of user, and a Bayesian method is used to update the probability in condition of all the responses; 2) we take the interaction of the frames into consideration and select a proper frame set for video summarization. We verified that the relevance in different people's mind is not totally irrelevant. A relevance distance based on the characteristics of the video frames and the trend of users' decision making is proposed for more accurate likelihood definition. Experiments showed that our approach could provide satisfied summarizations in acceptable iteration in most cases and demonstrated the efficiency of the interactive and feedback process. © 2013 ACM.; With the explosive growth of video resource, efficient techniques for generating video summarization are appealing for facilitating understanding and presenting video content. Traditional video summarizations were usually given through extracting key frames based on the features of frames in video sequence. However, in many cases, the given key frames don't meet the key frames reside in the mind of users. In this paper, we propose an innovative approach based on relevance feedback to select the key frames of a video sequence for video summarization considering users' subjective visual preference. A two-step strategy to select the key frames is given. 1) we evaluate the preference of user, and a Bayesian method is used to update the probability in condition of all the responses; 2) we take the interaction of the frames into consideration and select a proper frame set for video summarization. We verified that the relevance in different people's mind is not totally irrelevant. A relevance distance based on the characteristics of the video frames and the trend of users' decision making is proposed for more accurate likelihood definition. Experiments showed that our approach could provide satisfied summarizations in acceptable iteration in most cases and demonstrated the efficiency of the interactive and feedback process. © 2013 ACM.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16664
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Ma, Cuixia ,Wen, Ruri ,Zeng, Dejun ,et al. A framework for key-frame selection based on relevance feedback[C]. Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States,2013:259-264.