FASUM: Feature Accelerated Single-Video Summarization

Plenty of video stuff is created, broadcasted, shared and stored each and every day by industry experts, beginners, and hobbyists. Video summaries aim at showcasing the semantics and content of a clip in reduced time and space to enable a quick overview of video clip relevance. This paper focuses on static summaries showing key frames from the video. The key frames are extracted by leveraging the feature extraction and deep feature learning algorithm. The video summarization finds its application in semantic visualization, copy detection, indexing and browsing, concept detection, video services, video sharing and searching, video streaming, video caching, video quality assessment, and video transfer and video object tracking.