Next Article in Journal
A Fuzzy Path Selection Strategy for Aircraft Landing on a Carrier
Previous Article in Journal
Simulation Analysis and Experimental Study of the Cooker Hoods of High-Rise Residential Buildings
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(5), 778;

Similarity Estimation for Large-Scale Human Action Video Data on Spark

Data and Knowledge Engineering Lab, Department of Computer Science and Engineering, Kyung Hee University, Suwon 446-701, Korea
Author to whom correspondence should be addressed.
Received: 6 April 2018 / Revised: 26 April 2018 / Accepted: 8 May 2018 / Published: 14 May 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
Full-Text   |   PDF [1274 KB, uploaded 14 May 2018]   |  


The amount of human action video data is increasing rapidly due to the growth of multimedia data, which increases the problem of how to process the large number of human action videos efficiently. Therefore, we devise a novel approach for human action similarity estimation in the distributed environment. The efficiency of human action similarity estimation depends on feature descriptors. Existing feature descriptors such as Local Binary Pattern and Local Ternary Pattern can only extract texture information but cannot obtain the object shape information. To resolve this, we introduce a new feature descriptor, namely Edge based Local Pattern descriptor (ELP). ELP can extract object shape information besides texture information and ELP can also deal with intensity fluctuations. Moreover, we explore Apache Spark to perform feature extraction in the distributed environment. Finally, we present an empirical scalability evaluation of the task of extracting features from video datasets. View Full-Text
Keywords: human action similarity estimation; feature extraction; edge detection human action similarity estimation; feature extraction; edge detection

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Xu, W.; Uddin, M.A.; Dolgorsuren, B.; Akhond, M.R.; Khan, K.U.; Hossain, M.I.; Lee, Y.-K. Similarity Estimation for Large-Scale Human Action Video Data on Spark. Appl. Sci. 2018, 8, 778.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top