Commercial Video Evaluation via Low-Level Feature Extraction and Selection
To discover the influence of the commercial videos’ low-level features on the popularity of the videos, the feature selection method should be used to get the video features influencing the videos’ evaluation mostly after analyzing the source data and the audiences’ evaluations of the videos. After...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2018/2056381 |
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| _version_ | 1849397648836001792 |
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| author | Xiangmin Lun Mingxuan Wang Zhenglin Yu Yimin Hou |
| author_facet | Xiangmin Lun Mingxuan Wang Zhenglin Yu Yimin Hou |
| author_sort | Xiangmin Lun |
| collection | DOAJ |
| description | To discover the influence of the commercial videos’ low-level features on the popularity of the videos, the feature selection method should be used to get the video features influencing the videos’ evaluation mostly after analyzing the source data and the audiences’ evaluations of the videos. After extracting the low-level features of the videos, this paper improved the Correlation-Based Feature Selection (CFS) method which is widely used and proposed an algorithm named CFS-Spearmen which combined the Spearmen correlation coefficient and the classical CFS to select features. The 4 datasets in UCI machine learning database were employed as the experiment data. The experiment results were compared with the results using traditional CFS, Minimum Redundancy and Maximum Relevance (mRMR). The SVM was used to test the method in this paper. Finally, the proposed method was used in commercial videos’ feature selection and the most influential feature set was obtained. |
| format | Article |
| id | doaj-art-aca2c23187c94433bbf36c27ece6eb43 |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-aca2c23187c94433bbf36c27ece6eb432025-08-20T03:38:55ZengWileyAdvances in Multimedia1687-56801687-56992018-01-01201810.1155/2018/20563812056381Commercial Video Evaluation via Low-Level Feature Extraction and SelectionXiangmin Lun0Mingxuan Wang1Zhenglin Yu2Yimin Hou3College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin, ChinaCollege of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin, ChinaTo discover the influence of the commercial videos’ low-level features on the popularity of the videos, the feature selection method should be used to get the video features influencing the videos’ evaluation mostly after analyzing the source data and the audiences’ evaluations of the videos. After extracting the low-level features of the videos, this paper improved the Correlation-Based Feature Selection (CFS) method which is widely used and proposed an algorithm named CFS-Spearmen which combined the Spearmen correlation coefficient and the classical CFS to select features. The 4 datasets in UCI machine learning database were employed as the experiment data. The experiment results were compared with the results using traditional CFS, Minimum Redundancy and Maximum Relevance (mRMR). The SVM was used to test the method in this paper. Finally, the proposed method was used in commercial videos’ feature selection and the most influential feature set was obtained.http://dx.doi.org/10.1155/2018/2056381 |
| spellingShingle | Xiangmin Lun Mingxuan Wang Zhenglin Yu Yimin Hou Commercial Video Evaluation via Low-Level Feature Extraction and Selection Advances in Multimedia |
| title | Commercial Video Evaluation via Low-Level Feature Extraction and Selection |
| title_full | Commercial Video Evaluation via Low-Level Feature Extraction and Selection |
| title_fullStr | Commercial Video Evaluation via Low-Level Feature Extraction and Selection |
| title_full_unstemmed | Commercial Video Evaluation via Low-Level Feature Extraction and Selection |
| title_short | Commercial Video Evaluation via Low-Level Feature Extraction and Selection |
| title_sort | commercial video evaluation via low level feature extraction and selection |
| url | http://dx.doi.org/10.1155/2018/2056381 |
| work_keys_str_mv | AT xiangminlun commercialvideoevaluationvialowlevelfeatureextractionandselection AT mingxuanwang commercialvideoevaluationvialowlevelfeatureextractionandselection AT zhenglinyu commercialvideoevaluationvialowlevelfeatureextractionandselection AT yiminhou commercialvideoevaluationvialowlevelfeatureextractionandselection |