Parallelizing Multi-featured Content Based Search and Retrieval of Videos through High Performance Computing

Video Retrieval is an important technology that helps to design video search engines and allow users to browse and retrieve videos of interest from huge databases. Though, there are many existing techniques to search and retrieve videos based on spatial and temporal features but are unable to perform well resulting in high ranking of irrelevant videos leading to poor user satisfaction. In this paper an efficient multi-featured method for matching and extraction is proposed in parallel paradigm to retrieve videos accurately and quickly from the collection. Proposed system is tested on datasets that contains various categories of videos of varying length such as traffic, sports, nature etc. Experimental results show that around 80% of accuracy is achieved in searching and retrieving video. Through the use of high performance computing, the parallel execution performs 5 times faster in locating and retrieving videos of intrest than the sequential execution.