TY  - JOUR
AU  - Mohammad Ali Bagheri
AU  - Qigang  Gao
AU  - Sergio Escalera
AU  - Huamin Ren
AU  - Thomas B. Moeslund
AU  - Elham Etemad
PY  - 2017//
TI  - Locality Regularized Group Sparse Coding for Action Recognition
T2  - CVIU
JO  - Computer Vision and Image Understanding
SP  - 106
EP  - 114
VL  - 158
KW  - Bag of words
KW  - Feature encoding
KW  - Locality constrained coding
KW  - Group sparse coding
KW  - Alternating direction method of multipliers
KW  - Action recognition
N2  - Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets.
UR  - https://doi.org/10.1016/j.cviu.2017.02.008
N1  - HuPBA; no proj
ID  - Mohammad Ali Bagheri2017
ER  -