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.
Highlights
Digital data plays an essential role in our life
Video retrieval is a vital process in multimedia applications such as video search engines, digital museums, and video-on-demand broadcasting
As the concept detectors [8] play a vital role in semantic video retrieval, a thorough study of the obstacles that face the construction of the generic concept detectors will be presented
Summary
Digital data plays an essential role in our life. The digital data include videos, images, documents, sounds, etc. The causation of the semantic gap is the difference between user requirements which are represented in queries and the low-level representation of videos on the storage media. The definition and the causes of a semantic gap in video retrieval will be explored. As the concept detectors [8] play a vital role in semantic video retrieval, a thorough study of the obstacles that face the construction of the generic concept detectors will be presented. The different methods model semantic concept relationships in video retrieval are categorized and explained in more details. 4) We present the novel definitions of the different methods of semantic video retrieval. Subsection 2.2 in Section 2 discusses the semantic gap. Subsection 2.4 in Section 2 discusses the different methods of semantic video retrieval
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