FOR most video databases, browsing as a means of retrieval is impractical, and query-based searching is required. Queries are often expressed as keywords, requiring the videos to be tagged. In view of the ever-increasing size of video databases, the assumption of an appropriate and complete set of tags might be invalid, and content-based video retrieval techniques become vital. Video is an example of multimedia information, which contains a huge set of raw data, richer content information, and made up of continuous still images. This makes the retrieval of videos more tedious and challenging. The content-based video retrieval has attracted the users as the use of multimedia data has been increased drastically in day-to-day life compared to text-based search. Text-based search or retrieval is good when the database is small and limited. But when it comes to huge and larger databases or data warehouses, content-based retrieval plays a vital role. Hence making the system more robust and reliable as well as automatized is necessary for the domain of the CBVR system. The CBVR basically retrieves a related video from the database based on visual contents like color, texture, edge, shape, and motion.

• Mohammed Obada Bahaa Mohammed Sarsar (16W0061)
• Mostafa Ashraf Mohammed (16T0107)
• Ali Said Mohammed Ali (1600832)
• Ghada Ragab Abdelnabi (1600953)
• Mariam Hamdy Abdelmaksoud Afify (1601367)
• Mariam Atef Shafik (1601372)
• Mary Malak Labib Eskander (1601069)
• Mai AboElmaaty Mohame (1401469)

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. 
 See deployment for notes on how to deploy the project on a live system.

1. PyQt5 
2. git clone: https://github.com/Mostafa-ashraf19/content-based-retrieval-system.git
3. Run it