Abstract: Concerning that most Web video categorization researches just focus on the basic simple fusion of the information from text model and visual model, a Web video classification method based on the bidirectional propagation of heterogeneous attributes was proposed. Firstly, the method adopted K-means clustering to divide key frames into multiple clusters, and modeled videos at the level of frame. For each cluster, a part of key frames were randomly chosen to propagate their text information to the cluster. For each key frame, the text explanation of the corresponding cluster was transferred to this frame. Finally, the Web video was classified based on the extended text information from dual propagation by using Support Vector Machine (SVM) classifiers. The method integrates heterogeneous attributes well based on the dual propagation. The experimental results demonstrate the effectiveness of the method in the Web video classification.

Key words: Web video classification, heterogeneous data, visual modality, textual modality, bidirectional propagation

摘要： 针对以往大多数网络视频分类研究只将文本和视觉特征进行简单融合的问题，提出了基于异构信息双向传播的网络视频分类方法。首先基于K均值方法将视频关键帧聚类成多个簇，在帧层次上对视频数据进行建模；将每个簇中代表性关键帧的文本信息传播至该簇作为其文本解释，完成从文本至视觉模态的传播；对每个关键帧，将其对应簇的文本解释传播至该关键帧，完成从视觉至文本模态的传播；最后基于支持向量机(SVM)对网络视频进行分类。在信息的双重传播中两类异构数据得到了密切的融合。实验结果表明该方法有效地提高了网络视频分类的准确率。

关键词: 网络视频分类, 异构数据, 视觉模态, 文本模态, 双向传播