Commercial Video Evaluation via Low-Level Feature Extraction and Selection. (10th October 2018)

• Record Type: Journal Article
• Authors: Lun, Xiangmin; Wang, Mingxuan; Yu, Zhenglin; Hou, Yimin
• Other Names: Loui Alexander Academic Editor.
• Is Part Of: Advances in multimedia. Volume 2018(2018)
• Publication Date: 2018-10-10
• Subjects: Multimedia systems -- Periodicals; Computer networks -- Periodicals; Multimédia; Réseaux d'ordinateurs; Computer networks; Multimedia systems; Periodicals; 006.7
• DOI: 10.1155/2018/2056381
• Languages: English
• ISSNs: 1687-5680

Abstract

Abstract : 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.