基于融合MPEG-7描述子和二次预测机制的视频自动分类算法

An Automatic Video Classification Scheme Based on Combination of MPEG-7 Descriptors and Second-Prediction Strategy

针对互联网上日益增长的视频数量,提出了一种大量融合MPEG-7描述子并启用二次预测机制的视频自动分类方法.研究了颜色、纹理、形状、运动等9种MPEG-7描述子,从5类视频中提取并融合这些描述子作为视频的整体特征,输入支持向量机(SVM)中进行模型训练和预测.在传统支持向量机的1-1方法中,通过启用二次预测机制来提高分类的准确率.实验结果表明,该方法与其他方法相比有较高的准确率,适合大规模、复杂环境下的视频自动分类任务.

To deal with the growing amount of videos on the Internet,this paper presented a scheme for automatic video classification based on the combination of MPEG-7 descriptors and second-prediction strategy.Nine MPEG-7 descriptors such as color,texture,shape and motion were extracted from five different genres of videos and combined as a whole representative feature.Then it was put into an SVM classifier to train the model and predict.The traditional 1-1 method was modified with a second-prediction strategy to improve the classification accuracy.The experiments on a broad range of video data demonstrate that the accuracy of our classification scheme is higher than other existing schemes and the scheme is suitable for the large-scale video classification task under a complex environment.