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Adaptive Greedy Dictionary Selection for Web Media Summarization
Cong Y(丛杨); Liu J(刘霁); Sun G(孙干); You, Quanzeng; Li, Yuncheng; Luo JB(罗杰波)
作者部门机器人学研究室
关键词Sparse Representation Norm Dictionary Learning Dictionary Selection Forward-backward Greedy Method
发表期刊IEEE Transactions on Image Processing
ISSN1057-7149
2017
卷号26期号:1页码:185-195
收录类别SCI ; EI
EI收录号20170703341054
WOS记录号WOS:000402822500014
产权排序1
资助机构NSFC under Grant 61375014 and Grant 61533015, in part by the CAS-Youth Innovation Promotion Association Scholarship under Grant 2012163, and in part by the foundation of Chinese Scholarship Council.
摘要Initializing an effective dictionary is an indispensable step for sparse representation. In this paper, we focus on the dictionary selection problem with the objective to select a compact subset of basis from original training data instead of learning a new dictionary matrix as dictionary learning models do. We first design a new dictionary selection model via l2,0 norm. For model optimization, we propose two methods: one is the standard forward-backward greedy algorithm, which is not suitable for large-scale problems; the other is based on the gradient cues at each forward iteration and speeds up the process dramatically. In comparison with the state-of-the-art dictionary selection models, our model is not only more effective and efficient, but also can control the sparsity. To evaluate the performance of our new model, we select two practical web media summarization problems: 1) we build a new data set consisting of around 500 users, 3000 albums, and 1 million images, and achieve effective assisted albuming based on our model and 2) by formulating the video summarization problem as a dictionary selection issue, we employ our model to extract keyframes from a video sequence in a more flexible way. Generally, our model outperforms the state-of-the-art methods in both these two tasks.
语种英语
WOS标题词Science & Technology ; Technology
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
关键词[WOS]K-SVD ; SIGNAL RECONSTRUCTION ; LEARNING ALGORITHM ; DANTZIG SELECTOR ; CONSUMER VIDEOS ; SPARSE ; REPRESENTATION ; RECOGNITION ; FRAMEWORK ; PURSUIT
WOS研究方向Computer Science ; Engineering
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文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/19726
专题机器人学研究室
通讯作者Cong Y(丛杨)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Department of Computer Science, University of Rochester, Rochester, NY, 14611, United States
推荐引用方式
GB/T 7714
Cong Y,Liu J,Sun G,et al. Adaptive Greedy Dictionary Selection for Web Media Summarization[J]. IEEE Transactions on Image Processing,2017,26(1):185-195.
APA Cong Y,Liu J,Sun G,You, Quanzeng,Li, Yuncheng,&Luo JB.(2017).Adaptive Greedy Dictionary Selection for Web Media Summarization.IEEE Transactions on Image Processing,26(1),185-195.
MLA Cong Y,et al."Adaptive Greedy Dictionary Selection for Web Media Summarization".IEEE Transactions on Image Processing 26.1(2017):185-195.
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