Spatially regularized and locality-constrained linear coding for human action recognition |
| |
Authors: | Bin Wang Wen Gai Shouchun Guo Yu Liu Wei Wang Maojun Zhang |
| |
Institution: | 1. Facility Design and Instrumentation Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, P. R. China 2. College of Information System and Management, National University of Defense Technology, Changsha, 410073, P. R. China
|
| |
Abstract: | To reduce quantization error, preserve the manifold of local features, distinguish the ambiguous features, and model the spatial configuration of features for Bag-of-Features (BoF) model-based human action recognition, a novel feature coding method called spatially regularized and locality-constrained linear coding (SLLC) is proposed. The spatial regularization and locality constraint are involved in the feature coding phase to model the spatial configuration of features and preserve their nonlinear manifold. The action recognition experimental results on benchmark datasets show that SLLC achieves better performance than the state-of-the-art feature coding methods such as soft vector quantization, sparse coding, and locality-constrained linear coding. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|