Recognizing human actions based on Sparse Coding with Non-negative and Locality constraints

In this paper, Sparse Coding with Non-negative and Locality constraints (SCNL) is proposed to generate discriminative feature descriptions for human action recognition. The non-negative constraint ensures that every data sample is in the convex hull of its neighbors. The locality constraint makes a data sample only represented by its related neighbor atoms. The sparsity constraint confines the dictionary atoms involved in the sample representation as fewer as possible. The SCNL model can better capture the global subspace structures of data than classical sparse coding, and are more robust to noise compared to locality-constrained linear coding. Extensive experiments testify the significant advantages of the proposed SCNL model through evaluations on three remarkable human action datasets.