Using Diverse Set of Features to Design a Content-Based Video Retrieval System Optimized by Gravitational Search Algorithm

This paper explains about the content-based video retrieval approach (CBVR) using four varieties of features and 12 distance measurements, which is optimised by gravitational search algorithm (GSA). Initially, CBVR technique extracts five kinds of features such as colour, texture, shape, image and audio features that belong to each frame. Consequently, it emerges particular distance measurements for every sort of features to compute the similarity between query frame and remaining in the database frame. In this paper, we have used GSA to find the nearly optimal combination between the features and their respective similarity measurements. At last, from the video database, the query-based videos are recovered. For experimentation, here we used two types of databases such as sports video and UCF sports action datasets. The experimental results demonstrate that the proposed CBVR method shows better performance when contrasted with other existing methods.