Abstract
Event recognition in the consumer videos is a challenging task since it is difficult to collect a large number of labeled training videos. In this paper, we propose a novel Multiple kernel Image Group Adaptation approach to divide the training labeled Web images into several semantic groups and optimize the combinations of each based kernel. Our method simultaneously learns a kernel function and a robust Support Vector Regression (SVR) classifier by minimizing both the structure risk of SVR with the smooth assumption and the distribution difference of weighted image groups and the consumer videos. Comprehensive experiments on the datasets CCV and TREATED 2014 demonstrate the effectiveness of our method for event recognition.
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