Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1683
Title: Using Pre-trained Models As Feature Extractor To Classify Video Styles Used In MOOC Videos
Authors: Aryal, S
Porawagama, A. S
Hasith, M. G. S
Thoradeniya, S. C
Kodagoda, N
Suriyawansa, K
Keywords: Using Pre-trained Models
Feature Extractor
Classify Video Styles
MOOC Videos
Issue Date: 21-Dec-2018
Publisher: IEEE
Citation: S. Aryal, A. S. Porawagama, M. G. S. Hasith, S. C. Thoradeniya, N. Kodagoda and K. Suriyawansa, "Using Pre-trained Models As Feature Extractor To Classify Video Styles Used In MOOC Videos," 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), 2018, pp. 1-5, doi: 10.1109/ICIAFS.2018.8913347.
Series/Report no.: 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS);Pages 1-5
Abstract: Massive Open Online Courses (MOOCs) have emerged as new learning phenomenon in the field of e-learning. Over recent years, it has attracted a significant number of learners as well as researchers. A wide range of researches is being carried out across multiple aspects of MOOCs. Video lectures are the most fundamental component in a MOOC. There are standard video styles that are normally used across several MOOC platforms, such as, talking head, demonstration, slides, animation etc. This paper presents an Image-Based classification approach of the video styles where a single video is split into multiple image frames, and then each frame is classified into one of the video style-category. Different classifier models built on top of each state-of-the-art deep neural architectures, including VGG16, InceptionV3, and ResNet50 are evaluated and the comparison of results is shown. Furthermore, the paper also discusses a numeric method to calculate the composition level of a single video style in multi-style filed videos based on the classification results.
URI: http://rda.sliit.lk/handle/123456789/1683
ISSN: 2151-1810
Appears in Collections:Department of Computer Science and Software Engineering -Scopes
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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