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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)
Department机器人学研究室
Source PublicationIEEE Transactions on Image Processing
ISSN1057-7149
2017
Volume26Issue:1Pages:185-195
Indexed BySCI ; EI
EI Accession number20170703341054
WOS IDWOS:000402822500014
Contribution Rank1
Funding OrganizationNSFC 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.
KeywordSparse Representation Norm Dictionary Learning Dictionary Selection Forward-backward Greedy Method
AbstractInitializing 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.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS KeywordK-SVD ; SIGNAL RECONSTRUCTION ; LEARNING ALGORITHM ; DANTZIG SELECTOR ; CONSUMER VIDEOS ; SPARSE ; REPRESENTATION ; RECOGNITION ; FRAMEWORK ; PURSUIT
WOS Research AreaComputer Science ; Engineering
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/19726
Collection机器人学研究室
Corresponding AuthorCong Y(丛杨)
Affiliation1.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
Recommended Citation
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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