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Educational video classification by using a transcript to image transform and supervised learning

Chatbri, Houssem, Oliveira, Marlon orcid logoORCID: 0000-0003-0528-3807, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471, Kameyama, Keisuke, Kwan, Paul, Sutherland, Alistair and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2017) Educational video classification by using a transcript to image transform and supervised learning. In: International Conference on Image Processing Theory, Tools and Applications (IPTA), 28 Nov - 1 Dec 2017, Montreal, Canada. ISBN 978-1-5386-1842-4

Abstract
In this work, we present a method for automatic topic classification of educational videos using a speech transcript transform. Our method works as follows: First, speech recognition is used to generate video transcripts. Then, the transcripts are converted into images using a statistical co-occurrence transformation that we designed. Finally, a classifier is used to produce video category labels for a transcript image input. For our classifiers, we report results using a convolutional neural network (CNN) and a principal component analysis (PCA) model. In order to evaluate our method, we used the Khan Academy on a Stick dataset that contains 2,545 videos, where each video is labeled with one or two of 13 categories. Experiments show that our method is effective and strongly competitive against other supervised learning-based methods.
Metadata
Item Type:Conference or Workshop Item (Speech)
Event Type:Conference
Refereed:Yes
Additional Information:Research Centre: Insight Centre for Data Analytics
Uncontrolled Keywords:Educational video classification; transcript features; convolutional neural networks (CNN); principal component analysis (PCA)
Subjects:Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Multimedia systems
Computer Science > Digital video
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:IEEE Computer Society
Copyright Information:© 2017 IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council for Science Engineering and Technology, SFI/12/RC/2289
ID Code:22185
Deposited On:10 Jan 2018 15:51 by Houssem Chatbri . Last Modified 07 Feb 2019 10:35
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