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Integrating word embeddings and document topics with deep learning in a video classification framework
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (Computer Science)ORCID iD: 0000-0002-0199-2377
Norwegian University of Science and Technology, Norway.
(Computer Science)ORCID iD: 0000-0003-0512-6350
2019 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 128, p. 85-92Article in journal (Refereed) Published
Abstract [en]

The advent of MOOC platforms brought an abundance of video educational content that made the selection of best fitting content for a specific topic a lengthy process. To tackle this challenge in this paper we report our research efforts of using deep learning techniques for managing and classifying educational content for various search and retrieval applications in order to provide a more personalized learning experience. In this regard, we propose a framework which takes advantages of feature representations and deep learning for classifying video lectures in a MOOC setting to aid effective search and retrieval. The framework consists of three main modules. The first module called pre-processing concerns with video-to-text conversion. The second module is transcript representation which represents text in lecture transcripts into vector space by exploiting different representation techniques including bag-of-words, embeddings, transfer learning, and topic modeling. The final module covers classifiers whose aim is to label video lectures into the appropriate categories. Two deep learning models, namely feed-forward deep neural network (DNN) and convolutional neural network (CNN) are examined as part of the classifier module. Multiple simulations are carried out on a large-scale real dataset using various feature representations and classification techniques to test and validate the proposed framework.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 128, p. 85-92
Keywords [en]
Deep learning, Video classification, Embedding, Document topics, CNN, DNN
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Information Systems
Identifiers
URN: urn:nbn:se:lnu:diva-88861DOI: 10.1016/j.patrec.2019.08.019OAI: oai:DiVA.org:lnu-88861DiVA, id: diva2:1347067
Available from: 2019-08-30 Created: 2019-08-30 Last updated: 2019-09-06Bibliographically approved

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Kastrati, ZenunKurti, Arianit

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf