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中文摘要：
人体行为识别是计算机视觉研究的热点问题,现有的行为识别方法都是基于监督学习框架.为了取得较好的识别效果,通常需要大量的有标记样本来建模.然而,获取有标记样本是一个费时又费力的工作.为了解决这个问题,对半监督学习中的协同训练算法进行改进,提出了一种基于多学习器协同训练模型的人体行为识别方法.这是一种基于半监督学习框架的识别算法.该方法首先通过基于Q统计量的学习器差异性度量选择算法来挑取出协同训练中基学习器集,在协同训练过程中,这些基学习器集对未标记样本进行标记;然后,采用了基于分类器成员委员会的标记近邻置信度计算公式来评估未标记样本的置信度,选取一定比例置信度较高的未标记样本加入到已标记的训练样本集并更新学习器来提升模型的泛化能力.为了评估算法的有效性,采用混合特征来表征人体行为,从而可以快速完成识别过程.实验结果表明,所提出的基于半监督学习的行为识别系统可以有效地辨识视频中的人体动作.
英文摘要：
Human action recognition is a hot topic in computer vision. Most of the existing work use the action models based on supervised learning algorithms. To achieve good performance on recognition, a large amount of labeled samples are required to train the sophisticated action models. However, collecting labeled samples is labor-intensive. This paper presents a novel semi-supervised learning algorithm named multi-learner co-training model （MCM） to recognize human actions. Two key issues are addressed in this paper. Firstly, the base classifiers in co-training are selected by Q statistic-based classifiers selection algorithm （QSCSA）. Secondly, MCM is employed for the semi-supervised model construction. The new confidence score measure of unlabeled sample depends on estimating the classifier companion committee （CCC） accuracy on labeling the neighborhood of an unlabeled examples. To evaluate the proposed algorithm, mixed-descriptors are used to express actions so that the recognition algorithm can quickly complete the recognition process from a single frame of visual image. Experimental results are presented to show that the proposed semi-supervised learning system can recognize simple human actions effectively.
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