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Video Classification Methods: Multimodal Techniques

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Recent Trends in Computer Applications

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Abstract

With the increased usage of mobile electronic devices, social media platforms, and electronic-based applications in everyday life, the upload and usage of multimedia clips is increasing exponentially every year. There are many research initiatives tackling the challenge of multimodal video classification and it has been found that audio-based classification is less computationally expensive and just as effective, in many cases. However, research targeted toward acoustic-based detection is still in its initial stages. Audio-content-based classification pertains to several domains: music and speech signal processing, which are relatively popular research interests, and event, genre, and scene-based classification, which are still areas that need a lot of development. There is also a problem with the difficulty of assessing performances of different systems with a unified audio dataset, due to the lack of development in this field. In light of the vast proliferation of raw digital Arabic data, specifically videos, over the Internet, uncategorized and unused, we propose a system to target this problem. The proposed system design consists of visual features extraction and classification, combined with audio-based event classification, as well as semantic-content processing. Results are to be combined and documented using multimedia classification fusion techniques. We also propose to develop a new Arabic dataset based on news channel videos as well as raw videos from various online sources for testing and evaluation.

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Acknowledgment

This publication was made possible by GSRA grant # 1-1-1202-13026 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the author(s).

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Authors and Affiliations

  1. Qatar University, Computer Science and Engineering Department, Doha, Qatar

    Amal Dandashi & Jihad Mohamad Alja’am

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  1. Amal Dandashi
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  2. Jihad Mohamad Alja’am
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Correspondence to Jihad Mohamad Alja’am .

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Editors and Affiliations

  1. Computer Science and Engineering, Qatar University, Doha, Qatar

    Jihad Mohamad Alja’am

  2. Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada

    Abdulmotaleb El Saddik

  3. Electronic and Computer Engineering, Brunel University London, Uxbridge, UK

    Abdul Hamid Sadka

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Dandashi, A., Alja’am, J.M. (2018). Video Classification Methods: Multimodal Techniques. In: Alja’am, J., El Saddik, A., Sadka, A. (eds) Recent Trends in Computer Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-89914-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-89914-5_3

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