RANK-1 TENSOR PROJECTION VIA REGULARIZED REGRESSION FOR ACTION CLASSIFICATION
This paper proposes a novel method for classification using rank-1 tensor projection via regularized regression to directly map tensor example to its corresponding label, which is different from the general procedure of classification on the compact representation of the original data. Action can be naturally considered as a third-order tensor, where the first two dimensions represent the space and the third one is temporal. By applying this method to multi-label action classification based on problem transformation and subset embedding technique, we obtain the comparable results to the state-of-the-art approaches on the popular action datasets Weizmann and KTH. Our experimental results are also considerably robust to the viewpoint changes, the partial occlusion and the irregularities in the motion styles.
action classification action recognition tensor analysis ridge regression