@inproceedings{962a5b5845d04d188a117489516bba55,
title = "Iterative keyframe selection by orthogonal subspace projection",
abstract = "Recent developments on sparse dictionary selection have demonstrated promising results for Video Summarization (VS). However, the convex relaxation based solution cannot ensure the sparsity of the dictionary directly. In this paper, a selection matrix is proposed to model the VS problem, according to which the L0 norm of this selection matrix is imposed to ensure sparsity directly. As a result, a computational efficient Orthogonal Subspace Projection (OSP) based Iterative Keyframe Selection (IKS) algorithm is proposed for VS. In addition, a Percentage Of Reconstruction (POR) criterion is proposed to provide an intuitive and flexible control of the length of final video summaries even without prior knowledge of a given video. Experimental results on a popular benchmark dataset demonstrate that our proposed algorithm outperforms the state-of-the-art methods.",
keywords = "keyframe selection, Orthogonal subspace projection (OSP), sparse dictionary, sparse reconstruction, Video Summarization (VS)",
author = "Shaohui Mei and Genliang Guan and Zhiyong Wang and Mingyi He and Shuai Wan and Feng, {David Dagan}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.",
year = "2014",
month = jan,
day = "28",
doi = "10.1109/ICIP.2014.7025581",
language = "英语",
series = "2014 IEEE International Conference on Image Processing, ICIP 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2874--2878",
booktitle = "2014 IEEE International Conference on Image Processing, ICIP 2014",
}