Towards scalable summarization of consumer videos via sparse dictionary selection
The rapid growth of consumer videos requires an effective and efficient content summarization method to provide a user-friendly way to manage and browse the huge amount of video data. Compared with most previous methods that focus on sports and news videos, the summarization of personal videos is mo...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/97228 http://hdl.handle.net/10220/11469 |
| _version_ | 1811682617378996224 |
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| author | Cong, Yang Yuan, Junsong Luo, Jiebo |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Cong, Yang Yuan, Junsong Luo, Jiebo |
| author_sort | Cong, Yang |
| collection | NTU |
| description | The rapid growth of consumer videos requires an effective and efficient content summarization method to provide a user-friendly way to manage and browse the huge amount of video data. Compared with most previous methods that focus on sports and news videos, the summarization of personal videos is more challenging because of its unconstrained content and the lack of any pre-imposed video structures. We formulate video summarization as a novel dictionary selection problem using sparsity consistency, where a dictionary of key frames is selected such that the original video can be best reconstructed from this representative dictionary. An efficient global optimization algorithm is introduced to solve the dictionary selection model with the convergence rates as O(1/K2) (where K is the iteration counter), in contrast to traditional sub-gradient descent methods of O(1/√K). Our method provides a scalable solution for both key frame extraction and video skim generation, because one can select an arbitrary number of key frames to represent the original videos. Experiments on a human labeled benchmark dataset and comparisons to the state-of-the-art methods demonstrate the advantages of our algorithm. |
| first_indexed | 2024-10-01T03:59:41Z |
| format | Journal Article |
| id | ntu-10356/97228 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:59:41Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/972282020-03-07T14:02:46Z Towards scalable summarization of consumer videos via sparse dictionary selection Cong, Yang Yuan, Junsong Luo, Jiebo School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The rapid growth of consumer videos requires an effective and efficient content summarization method to provide a user-friendly way to manage and browse the huge amount of video data. Compared with most previous methods that focus on sports and news videos, the summarization of personal videos is more challenging because of its unconstrained content and the lack of any pre-imposed video structures. We formulate video summarization as a novel dictionary selection problem using sparsity consistency, where a dictionary of key frames is selected such that the original video can be best reconstructed from this representative dictionary. An efficient global optimization algorithm is introduced to solve the dictionary selection model with the convergence rates as O(1/K2) (where K is the iteration counter), in contrast to traditional sub-gradient descent methods of O(1/√K). Our method provides a scalable solution for both key frame extraction and video skim generation, because one can select an arbitrary number of key frames to represent the original videos. Experiments on a human labeled benchmark dataset and comparisons to the state-of-the-art methods demonstrate the advantages of our algorithm. 2013-07-15T08:37:38Z 2019-12-06T19:40:23Z 2013-07-15T08:37:38Z 2019-12-06T19:40:23Z 2011 2011 Journal Article Cong, Y., Yuan, J., & Luo, J. (2012). Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection. IEEE Transactions on Multimedia, 14(1), 66-75. 1520-9210 https://hdl.handle.net/10356/97228 http://hdl.handle.net/10220/11469 10.1109/TMM.2011.2166951 en IEEE transactions on multimedia © 2011 IEEE. |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Cong, Yang Yuan, Junsong Luo, Jiebo Towards scalable summarization of consumer videos via sparse dictionary selection |
| title | Towards scalable summarization of consumer videos via sparse dictionary selection |
| title_full | Towards scalable summarization of consumer videos via sparse dictionary selection |
| title_fullStr | Towards scalable summarization of consumer videos via sparse dictionary selection |
| title_full_unstemmed | Towards scalable summarization of consumer videos via sparse dictionary selection |
| title_short | Towards scalable summarization of consumer videos via sparse dictionary selection |
| title_sort | towards scalable summarization of consumer videos via sparse dictionary selection |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/97228 http://hdl.handle.net/10220/11469 |
| work_keys_str_mv | AT congyang towardsscalablesummarizationofconsumervideosviasparsedictionaryselection AT yuanjunsong towardsscalablesummarizationofconsumervideosviasparsedictionaryselection AT luojiebo towardsscalablesummarizationofconsumervideosviasparsedictionaryselection |