Joint classification of actions with matrix completion

Action classification is one of the crucial research areas with multitude applications. It has witnessed significant developments over last decade. In this paper, we propose to jointly classify actions from more than a single class using Matrix completion. Matrix-completion methods can handle the deficiencies in data very effectively resulting in improved classification accuracy. Features and labels from data are concatenated to form a big matrix with unknown or missing entries in the place of test data labels. Matrix-completion methods fill up these entries using tools from convex optimization resulting in classification. We show that the proposed method achieves improved performance over the recent works on two human action datasets including most popular Weizmann dataset and recently released and more realistic UCF-101 dataset.