Human action concentric video retrieval system using features weight updating method as relevance feedback

Retrieving videos based on its contents is becoming an increasingly popular area of research, because of enormous growth in the availability of multimedia information on public databases like Google and YouTube. Usually videos contain large variety of data but majority of online videos contain human as a subject of interest. In this paper, a human action based video retrieval system is presented which can be used to retrieve videos based on the contents of the query. The proposed system can search videos containing particular action on large databases efficiently. Furthermore, it is also shown that by using features weight updating approach as a Relevance feedback (RF), it is possible to involve user concepts interactively so that complex human action queries can be searched quickly to achieve useful results. Three popular Human action datasets namely Weizmann, KTH and UCF (sports) have been utilized in order to validate the performance of the proposed system. Experimental results and simulations show the efficacy of the proposed system. Even with number of visual challenges proposed approach will manage to get better accuracy as compare to other existing methods.