Large-scale Text-based Video Classification using Contextual Features

Zein Al Abidin Ibrahim, Siba Haidar and Ihab Sbeity
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Zein Al Abidin Ibrahim: Lebanese University-Faculty of Sciences
Siba Haidar: Lebanese University-Faculty of Sciences
Ihab Sbeity: Lebanese University-Faculty of Sciences

European Journal of Electrical Engineering and Computer Science, 2019, vol. 3, issue 2

Abstract: The production of video has increased and expanded dramatically. There is a need to reach accurate video classification. In our work, we use deep learning as a mean to accelerate the video retrieval task by classifying them into categories. We classify a video depending on the text extracted from it. We trained our model using fastText, a library for efficient text classification and representation learning, and tested our model on 15000 videos. Experimental results show that our approach is efficient and has good performance. Our technique can be used on huge datasets. It produces a model that can be used to classify any video into a specific category very quickly.

Keywords: Deep learning; Text-based Video Classification; Contextual Information; fastText (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejece0:v:3:y:2019:i:2:id:19068

DOI: 10.24018/ejece.2019.3.2.68

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