An efficient shot summarization method is presented based on agglomerative clustering of the shot frames. Unlike other agglomerative methods, our approach relies on a cluster merging criterion that computes the content homogeneity of a merged cluster. An important feature of the proposed approach is the automatic estimation of the number of a shot's most representative frames, called keyframes. The method starts by splitting each video sequence into small, equal sized clusters (segments). Then, agglomerative clustering is performed, where from the current set of clusters, a pair of clusters is selected and merged to form a larger unimodal (homogeneous) cluster. The algorithm proceeds until no further cluster merging is possible. At the end, the medoid of each of the final clusters is selected as keyframe and the set of keyframes constitutes the summary of the shot. Numerical experiments demonstrate that our method reasonable estimates the number of ground-truth keyframes, while extracting non-repetitive keyframes that efficiently summarize the content of each shot.

Date Published: 14 February 2015
PDF: 5 pages
Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 94451F (14 February 2015); doi: 10.1117/12.2180531

Published in SPIE Proceedings Vol. 9445:
Seventh International Conference on Machine Vision (ICMV 2014)
Antanas Verikas; Branislav Vuksanovic; Petia Radeva; Jianhong Zhou, Editor(s)