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Chatfield, K., Simonyan, K. and Zisserman, A. (2014) Efficient on the Fly Category Retrieval Using ConvNet and GPUs. In: Asian Conference on Computer Vision, Springer, Berlin, 129-145.

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• TITLE: Semantic-Based Video Retrieval Survey

  AUTHORS: Shaimaa Toriah Mohamed Toriah, Atef Zaki Ghalwash, Aliaa A. A. Youssif

  KEYWORDS: Semantic Video Retrieval, Concept Detectors, Context Based Concept Fusion, Semantic Gap

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.8, August 6, 2018

  ABSTRACT: There is a tremendous growth of digital data due to the stunning progress of digital devices which facilitates capturing them. Digital data include image, text, and video. Video represents a rich source of information. Thus, there is an urgent need to retrieve, organize, and automate videos. Video retrieval is a vital process in multimedia applications such as video search engines, digital museums, and video-on-demand broadcasting. In this paper, the different approaches of video retrieval are outlined and briefly categorized. Moreover, the different methods that bridge the semantic gap in video retrieval are discussed in more details.