Research On Automatic Video Classification Algorithm Based On Audio-visual Features And Svm

Automatic video classification is an important subject in the computer vision research area. It provides more convenience to the management of increasing video data nowadays, and is essentially crucial to the video broadcasting control technology. Based on the application of automatic video classification, the multimedia websites could classify the huge amount of video data automatically and easily, and further organize, store and index them more effectively. They could also use this technology to filter the bad video content primarily.The performance of video content classification largely relies on the video features and classifiers. This paper explores the problem from the view of video content and genres, presents the feature extraction method based on audio-visual features and the multi-class classification algorithms based on modified SVM.Based on the analysis of recent video classification algorithms, aiming at figuring out their problems, this paper presents a new feature combination model which includes both visual and audio features. Visual features consist of nine MPEG-7 visual descriptors, which include color, texture, shape and motion descriptors, while audio features consist of nine most useful time domain and frequency domain features. The features chosen enlarged the coherence of one class and difference of different classes, and reached a satisfied effect.After extracting the appropriate features, this paper describes the automatic video classification algorithm based on SVM and modifies the classify decision policies, namely, introduces the second-prediction strategy into the SVM 1-1 method, and improves the classification accuracy.Experiments show that: the features extracted have improved the identifiability of different video genres, and made them more easy to be classified; performance of the SVM classifier has been enhanced by introducing the second-prediction strategy; results compared with other video classification algorithms demonstrate the accuracy and effectiveness of this approach.