Title :
Key Frame Selection Based on KL-Divergence
Author :
Liangkai Li ; Qing Xu ; Xiaoxiao Luo ; Shihua Sun
Author_Institution :
Sch. of Comput. Sci. & Technol., Tianjin Univ., Tianjin, China
Abstract :
The key frame extraction is designed for obtaining a (very) compressed set of video frames that summarizes the essential content of a video sequence. In this paper, a well-known information theoretic measure, the Kullback-Leibler divergence (KLD), is studied to estimate the frame-by-frame distance between consecutive video images, for segmenting shots/sub shots and for choosing key frames. Our new key frame extraction method, which is effective and computationally fast, contributes to a good and quick understanding of a large amount of video data.
Keywords :
feature extraction; image segmentation; information theory; video signal processing; KL-divergence; Kullback-Leibler divergence; information theoretic measure; key frame extraction; key frame selection; subshots segmentation; video images; Cameras; Data mining; Entropy; Information theory; Measurement; Video sequences; Shots detection; key frame extraction; kl divergence;
Conference_Titel :
Multimedia Big Data (BigMM), 2015 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-8687-3
DOI :
10.1109/BigMM.2015.71
