Classifier learning methods commonly assume that the training data and the testing data are drawn from the same underlying distribution. However, in many practical situations, this assumption is violated. One example is the practical action videos with complex background and the universal human action databases of Kangliga Tekniska Hogskolan （KTH）. When training data are very scarce, supervised learning is difficult. However, it will cost lots of human and material resources to establish a labeled video set which includes a large amount of videos with complex backgrounds. In this paper, we propose an action recognition framework which uses transfer boosting learning algorithm. By using this algorithm, we can train an action recognition model fitting for most practical situations just relaying on the universal action video dataset and a tiny set of action videos with complex background. And the experiment results show that the performance is improved.
With the development of web 2.0, more and more social community applications appeared. The classical type of this kind of application is blog and facebook. The most important feature of these applications is that it is a self-media and users can post their own ideas in Internet. By using these social community applications, a big social network is formed. To study the feature of social network, it is important to mine the individual information at the beginning. In this paper, we propose a User Role based method to mine the relation between the user and object thing. First, we extract the User Role from the semantic dictionary Wordnet. Then, the feature of User Role is also mined by considering the hypemymy and hyponymy relation. Finally, we can use these features to deduce the User Role. In our experiments, we use a big corpus from TREC 2006 to test the mining performance. The experiment results show that the User Role effectively explores the feature of user.
