Weighted score-level feature fusion based on Dempster–Shafer evidence theory for action recognition

Guoliang Zhang; Songmin Jia; Xiuzhi Li; Xiangyin Zhang

doi:10.1117/1.JEI.27.1.013021

16 February 2018 Weighted score-level feature fusion based on Dempster–Shafer evidence theory for action recognition

Guoliang Zhang, Songmin Jia, Xiuzhi Li, Xiangyin Zhang

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Journal of Electronic Imaging, Vol. 27, Issue 1, 013021 (February 2018). https://doi.org/10.1117/1.JEI.27.1.013021

Abstract

The majority of human action recognition methods use multifeature fusion strategy to improve the classification performance, where the contribution of different features for specific action has not been paid enough attention. We present an extendible and universal weighted score-level feature fusion method using the Dempster–Shafer (DS) evidence theory based on the pipeline of bag-of-visual-words. First, the partially distinctive samples in the training set are selected to construct the validation set. Then, local spatiotemporal features and pose features are extracted from these samples to obtain evidence information. The DS evidence theory and the proposed rule of survival of the fittest are employed to achieve evidence combination and calculate optimal weight vectors of every feature type belonging to each action class. Finally, the recognition results are deduced via the weighted summation strategy. The performance of the established recognition framework is evaluated on Penn Action dataset and a subset of the joint-annotated human metabolome database (sub-JHMDB). The experiment results demonstrate that the proposed feature fusion method can adequately exploit the complementarity among multiple features and improve upon most of the state-of-the-art algorithms on Penn Action and sub-JHMDB datasets.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Guoliang Zhang, Songmin Jia, Xiuzhi Li, and Xiangyin Zhang "Weighted score-level feature fusion based on Dempster–Shafer evidence theory for action recognition," Journal of Electronic Imaging 27(1), 013021 (16 February 2018). https://doi.org/10.1117/1.JEI.27.1.013021

Received: 18 August 2017; Accepted: 16 January 2018; Published: 16 February 2018

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