Commercial Video Evaluation via Low-Level Feature Extraction and Selection

Xiangmin Lun,Yimin Hou,Mingxuan Wang,Zhenglin Yu

doi:10.1155/2018/2056381

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.

Highlights

As a major kind of commercial multimedia, commercial videos’ popularity is concerned by related companies and producers
The article [2] proposed an objective video quality evaluation method based on motion and disparity information
The article [3] presented a video quality evaluation method based on Quaternion Singular Value Decomposition

Summary

Introduction

As a major kind of commercial multimedia, commercial videos’ popularity is concerned by related companies and producers. For all the features extracted, we can select the most influential feature set through the feature selection methods according the commercial videos’ evaluation data. The main idea of feature selection is selecting a few valuable features and removing the useless ones from all the features extracted The methods, such as embedded methods, Relief [10], mRMR [11], and CFS [12, 13], are widely used now. The main idea of CFS is selecting the feature set with lower correlation between features and higher correlation between feature and class After this procedure, the redundant features and the features which were not closely related to the class would be removed. The results showed that the proposed method was better than CFS, mRMR, and lp-norm based sparsity regularized feature selection

Video Low-Level Features Extraction

Feature Selection Suing CFS

Experiment Analyzing for Feature Selection

Videos Popularity Prediction

Conclusions