Similarity Estimation for Large-Scale Human Action Video Data on Spark
AbstractThe 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
Share & Cite This Article
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.
Xu W, Uddin MA, Dolgorsuren B, Akhond MR, Khan KU, Hossain MI, Lee Y-K. Similarity Estimation for Large-Scale Human Action Video Data on Spark. Applied Sciences. 2018; 8(5):778.Chicago/Turabian Style
Xu, Weihua; Uddin, Md A.; Dolgorsuren, Batjargal; Akhond, Mostafijur R.; Khan, Kifayat U.; Hossain, Md I.; Lee, Young-Koo. 2018. "Similarity Estimation for Large-Scale Human Action Video Data on Spark." Appl. Sci. 8, no. 5: 778.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.