Commercial Video Evaluation via Low Level Feature Selection

Abstract

To discover the influence from the commercial videos’ low-level features to the popularity of the videos, the feature selection method should be used to get the video features influenced the videos’ evaluation mostly after analyzing the source data and the audiences’ evaluations of the videos. After extracting the low-level features of the his paper improved the Correlation-Based Feature Selection (CFS) 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 SVM was used to test the method in this paper. Finally, the proposed method was employed in commercial videos’ feature selection and the most influential feature set was chosen.