Recognizing Human Actions From Noisy Videos via Multiple Instance Learning

In this work, we study the task of recognizing human actions from noisy videos and effects of noise to recognition performance and propose a possible solution. Datasets available in computer vision literature are relatively small and could include noise due to labeling source. For new and relatively big datasets, noise amount would possible increase and the performance of traditional instance based learning methods is likely to decrease. In this work, we propose a multiple instance learning-based solution in case of an increase in noise. For this purpose, each video is represented with spatio-temporal features, then bag-of-words method is applied. Then, using support vector machines (SVM), both instance-based learning and multiple instance learning classifiers are constructed and compared. The classification results show that multiple instance learning classifiers has better performance than instance based learning counterparts on noisy videos.