Domain independent static video summarization using sparse autoencoders and K-means clustering

Abstract

 The ever growing video data over the internet has raised the challenge of efficient storage and retrieval of multimedia data. Video Summarization is one of the solutions to the problem which extracts interesting parts of a video. These summaries capture the essential content of the original video and enable the viewers to pick out interesting videos from huge video repositories by viewing its compact representation. To address the challenge of managing the video data, a new method for static video summarization is proposed here which is capable of describing input videos using a precise yet meaningful subset of frames. The method utilizes HOG descriptors of Gabor maps of input frames. A high-level representation of HOG descriptors is created using sparse auto-encoders (SAE). The final summary of the video is formed using candidate frames obtained after K-means clustering of these feature vectors. The summarization step is preceded by redundancy elimination to overcome computational burden. The method provides better results compared to the other state-of-the-art video summarization methods.