Multi-Max-Margin Support Vector Machine for multi-source human action recognition

We propose a new ensemble-based classifier for multi-source human action recognition called Multi-Max-Margin Support Vector Machine (MMM-SVM). This ensemble method incorporates the decision values of multiple sources and makes an informed final prediction by merging multi-source feature's intrinsic decision strength. Experiments performed on the benchmark IXMAS multi-view dataset (Weinland [1]) demonstrate that the performance of our multi-view system can further improve the accuracy over single view by 3–13% and consistently outperform the direct-concatenation method. We further apply this ensemble technique for combining the decision values of contextual and motion information in the UCF Sports dataset (Liu, 2009 [2]) and the results are comparable to the state-of-the-art, which exhibits our algorithm's potential for further extension in other areas of feature fusion problems.