- January 24, 2018
- Posted by: RSIS
- Categories: Computer Science and Engineering, Engineering, Information Technology
International Journal of Research and Scientific Innovation (IJRSI) | Volume IV, Issue XII, December 2017 | ISSN 2321–2705
Learning Ontology Rules for Semantic Video Retrieval
G Nagappa[1], G.Nageshwar[2] and E Sagar[3]
[1, 2, 3]Assistant Professor, Department of CSE, Malla Reddy of Engineering & Technology, Secunderbad, India
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. So, 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, video-on-demand broadcasting. The concept space is clustered, and the concepts which are near the clusters centroids are selected and called the bases concepts. In this paper, the different approaches of video retrieval are clearly and briefly categorized. Moreover, the different methods which try to bridge the semantic gap in video retrieval are discussed in more details.
Keywords: Semantic video retrieval; Concept detectors; Context based concept fusion, ontology enriched semantic space
I. INTRODUCTION
Digital data plays an essential role in our life. The digital data includes videos, images, documents, sound and etc. One of the most important digital media is video especially the dynamic one [1]. The video represents a rich source of information. The video can contain all the other digital data such as images, sound, and text. In addition, the video is characterized by its temporal consistency. The rapid progress of digital devices causes inflation in video database. Retrieving the required information from video database according to user needs is called a video retrieval process. A video retrieval is considered a branched field from the globalized one called information retrieval. Information retrieval is considered as a subfield of computer science that is concerned with the organization and retrieval of information from large database collections [2]. Video retrieval methods are important and essential for multimedia applications such as video search engines, digital museums, video-on-demand broadcasting, and etc. Video retrieval is still an active problem due the semantic gap, and the wide spread of social media and the enormous technological development.
