Heterogeneous multi-group adaptation for event recognition in consumer videos

Event recognition in consumer videos has attracted much attention from researchers. However, it is a very challenging task since annotating numerous training samples is time consuming and labor expensive. In this paper, we take a large number of loosely labeled Web images and videos represented by different types of features from Google and YouTube as heterogeneous source domains, to conduct event recognition in consumer videos. We propose a heterogeneous multi-group adaptation method to partition loosely labeled Web images and videos into several semantic groups and find the optimal weight for each group. To learn an effective target classifier, a manifold regularization is introduced into the objective function of Support Vector Regression (SVR) with an ϵ -insensitive loss. The objective function is alternatively solved by using standard quadratic programming and SVR solvers. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of our method.