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Open AccessArticle

Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition

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Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
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Cognitive Science Department, Xiamen University, Xiamen 361005, China
3
Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
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Faculty of Computer Science, University of Sunderland, Sunderland SR6 0DD, UK
*
Author to whom correspondence should be addressed.
Appl. Syst. Innov. 2019, 2(1), 7; https://doi.org/10.3390/asi2010007
Received: 31 December 2018 / Revised: 2 February 2019 / Accepted: 14 February 2019 / Published: 19 February 2019
(This article belongs to the Special Issue Healthcare System Innovation)
Egocentric video analysis is an important tool in healthcare that serves a variety of purposes, such as memory aid systems and physical rehabilitation, and feature extraction is an indispensable process for such analysis. Local feature descriptors have been widely applied due to their simple implementation and reasonable efficiency and performance in applications. This paper proposes an enhanced spatial and temporal local feature descriptor extraction method to boost the performance of action classification. The approach allows local feature descriptors to take advantage of saliency maps, which provide insights into visual attention. The effectiveness of the proposed method was validated and evaluated by a comparative study, whose results demonstrated an improved accuracy of around 2%. View Full-Text
Keywords: saliency map; local feature descriptors; egocentric action recognition; HOG; HMG; HOF; MBH saliency map; local feature descriptors; egocentric action recognition; HOG; HMG; HOF; MBH
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MDPI and ACS Style

Zuo, Z.; Wei, B.; Chao, F.; Qu, Y.; Peng, Y.; Yang, L. Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition. Appl. Syst. Innov. 2019, 2, 7.

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