Ranking Based Boosted Multiple Kernel Learning For Activity Recognition on First-Person Videos

In this paper, we investigate fusion of different types of classifiers for activity recognition on first-person videos in a data-driven approach. The algorithm first uses the classifiers, which are composed of kernel and descriptor combinations, through well-known AdaBoost trials. After all trials, classifiers are ordered and assigned ranks with respect to their performances in each trial separately. These classifiers compose a candidate list according to their performance ranks. Classifiers in the candidate list are employed together on the training set again. Classifiers in most successful candidate lists are combined as final classifiers. Our experiments show improvements in recognition comparison to traditional methods.