@Article{MohammadAliBagheri2017,
author="Mohammad Ali Bagheri
and Qigang  Gao
and Sergio Escalera
and Huamin Ren
and Thomas B. Moeslund
and Elham Etemad",
title="Locality Regularized Group Sparse Coding for Action Recognition",
journal="Computer Vision and Image Understanding",
year="2017",
volume="158",
pages="106--114",
optkeywords="Bag of words",
optkeywords="Feature encoding",
optkeywords="Locality constrained coding",
optkeywords="Group sparse coding",
optkeywords="Alternating direction method of multipliers",
optkeywords="Action recognition",
abstract="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.",
optnote="HuPBA; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3014), last updated on Tue, 22 May 2018 13:59:20 +0200",
opturl="https://doi.org/10.1016/j.cviu.2017.02.008"
}