Learning large scale of web video data requires considering unlabeled data and heterogeneous information.A novel online semi-supervised learning method is proposed for the web video classification,which adopts graphs as base classifiers on each view of texts and videos,and propagates labels by linear neighborhood propagation algorithm.The unlabeled data are chosen online with co-training strategy on multiple graphs and base classifiers are incrementally updated.The proposed method increases the classification accuracy and is suitable for online semi-supervised learning of large scale of web video data.Experimental results show that the average accuracy of this method is approximately 8.3 ％ higher than support vector machines,and the accuracy of learners after the online incremental learning increases by approximately 3 ％.%针对大规模网络视频数据的学习需要考虑无标签数据和异构信息的问题,提出了一种基于视觉和文本异构信息的网络视频在线半监督学习方法.该方法将文本和视觉看作2个视图,采用图作为基分类器对每个视图进行建模,并利用线性邻域的传播算法来预测样本类别.在不同视图之间采用多图上的协同训练,利用未标记样本增量地更新基分类器,并根据类别相关的融合方法确定最终结果,从而提高了分类准确率.实验结果表明,该方法的结果优于支持向量机方法约8.3％,在线增量更新后,学习器的性能提高了约3％,因此比较适合于大规模视频数据的在线半监督学习.
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