Semi-supervised action recognition in video via Labeled Kernel Sparse Coding and sparse L1 graph
Recognizing human actions in a video sequence using small number of labeled data are very desirable in practical applications. In this paper an action recognition approach via Labeled Kernel Sparse Coding (LKSC) and sparse L1 graph is proposed under the framework of semi-supervised learning framework. Inspired by the fact that kernel trick can capture the nonlinear similarity of features, we extend the sparse representation classifier (SRC) is extended to the empirical kernel projection space. By using the calculated sparse coding coefficients of both labeled and unlabeled samples as the graph weights, a sparse L1 graph is established in a parameter-free manner. Some experiments are taken on Weizmann human action datasets and a movie sequence of a ballet dance, to investigate the performance of our proposed method, and the results show that it is insensitive to the only parameter ?, and can achieve high recognition rate when using some number of labeled samples.
semi-supervised action recognition labeled kernel sparse coding sparse l1 graph