An efficient multi-feature SVM solver for complex event detection

Multimedia event detection (MED) has become one of the most important visual content analysis tools as the rapid growth of the user generated videos on the Internet. Generally, multimedia data is represented by multiple features and it is difficult to gain better performance for complex event detection with only single feature. However, how to fuse different features effectively is the crucial problem for MED with multiple features. Meanwhile, exploiting multiple features simultaneously in the large-scale scenarios always produces a heavy computational burden. To address these two issues, we propose a self-adaptive multi-feature learning framework with efficient Support Vector Machine (SVM) solver for complex event detection in this paper. Our model is able to utilize multiple features reasonably with an adaptively weighted linear combination manner, which is simple yet effective, according to the various impact that different features on a specific event. In order to mitigate the expensive computational cost, we employ a fast primal SVM solver in the proposed alternating optimization algorithm to obtain the approximate solution with gradient descent method. Extensive experiment results over standard datasets of TRECVID MEDTest 2013 and 2014 demonstrate the effectiveness and superiority of the proposed framework on complex event detection.