A Novel Key-Frame Detection Technique Using Statistical Run Test and Majority Voting

Detection of representative frames, also called key-frames, is essential for efficient indexing, browsing and retrieval of video data and also for video summarization. Once a video stream is segmented into shots, the representative frames or key-frames for the shot are selected. The number of such frames in a shot may vary depending on the variation in the content. Thus, for a wide variety of shots automatic selection of suitable number of representative frames still remains a challenge. In this work, we propose a novel scheme for key-frame detection by dividing an available shot into subshots using hypothesis testing and majority voting. Each subshot is supposed to be uniform in terms of visual content. Then for each subshot, the frame rendering the highest fidelity is extracted as the key-frame. Experimental result shows that the scheme works satisfactorily for a wide variety of shots.