Effective key‐frame extraction approach using TSTBTC–BBA

Abstract

Video summarisation and key-frame extraction play a vital role in video processing and content-based video analysis. Key-frame extraction facilitates rapid browsing and proficient video ordering in numerous applications such as multimedia, gaming etc. The application of ‘effective key-frame extraction’ can be used in the field of object detection and tracking, and it is found to be helpful in providing some sort of signal to visually impaired persons. In this study, the advantages of Thepade's sorted ternary block truncation coding (TSTBTC) along with binary bat algorithm (BBA) are used to overcome the limitations by extracting an effective key frame from the videos. Static images in the form of frames are extracted from input video database and processed through TSTBTC algorithm for calculating similarity measures amongst two consecutive frames. The input data is processed using four colour spaces, namely red–green–blue colour space, Kekre's LUV colour space, YCbCr colour space, YUV colour space and BBA are used to optimise the threshold value. Furthermore, five similarity measures indices are used to calculate the number of frames, and the result obtained shows that TSTBTC–BBA algorithm deployed in YUV colour space provides better results compared with other existing techniques regarding precision, F-measure and colour spaces.