A novel model for content based video classification of distributed datasets

We are now living in the age of Big Data. The amount of data being generated every single second is beyond comprehensible. Human capacity for manual data analysis is nowhere near the rate at which new data is being captured. Video data is one of the major contributors to this data flood. The sheer size of this data makes it impossible to store it on an independent node, let alone classify it. This paper proposes a novel model for classifying large amounts of video data stored on networked nodes. The proposed model uses a multi-system and multi-threaded approach for classifying large amounts of videos while making rigorous use of all the resources at its disposal.