Structured Sparse Coding With the Group  Log-regularizer for Key Frame Extraction

doi:10.1109/JAS.2022.105602

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	Structured Sparse Coding With the Group Log-regularizer for Key Frame Extraction
	Zhenni Li; Yujie Li; Benying Tan; Shuxue Ding; Shengli Xie
刊名	IEEE/CAA Journal of Automatica Sinica
	2022
卷号	9 期号:10 页码:1818-1830
关键词	Difference of convex algorithm (DCA) group log-regularizer key frame extraction structured sparse coding
ISSN号	2329-9266
DOI	10.1109/JAS.2022.105602
英文摘要	Key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire video. However, how to develop a key frame extraction algorithm that can automatically extract a few frames with a low reconstruction error remains a challenge. In this paper, we propose a novel model of structured sparse-coding-based key frame extraction, wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction error. To automatically extract key frames, a decomposition scheme is designed to separate the sparse coefficient matrix by rows. The rows enforced by the nonconvex group log-regularizer become zero or nonzero, leading to the learning of the structured sparse coefficient matrix. To solve the nonconvex problems due to the log-regularizer, the difference of convex algorithm (DCA) is employed to decompose the log-regularizer into the difference of two convex functions related to the l₁ norm, which can be directly obtained through the proximal operator. Therefore, an efficient structured sparse coding algorithm with the group log-regularizer for key frame extraction is developed, which can automatically extract a few frames directly from the video to represent the entire video with a low reconstruction error. Experimental results demonstrate that the proposed algorithm can extract more accurate key frames from most SumMe videos compared to the state-of-the-art methods. Furthermore, the proposed algorithm can obtain a higher compression with a nearly 18% increase compared to sparse modeling representation selection (SMRS) and an 8% increase compared to SC-det on the VSUMM dataset.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49711]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Zhenni Li,Yujie Li,Benying Tan,et al. Structured Sparse Coding With the Group Log-regularizer for Key Frame Extraction[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(10):1818-1830.
APA	Zhenni Li,Yujie Li,Benying Tan,Shuxue Ding,&Shengli Xie.(2022).Structured Sparse Coding With the Group Log-regularizer for Key Frame Extraction.IEEE/CAA Journal of Automatica Sinica,9(10),1818-1830.
MLA	Zhenni Li,et al."Structured Sparse Coding With the Group Log-regularizer for Key Frame Extraction".IEEE/CAA Journal of Automatica Sinica 9.10(2022):1818-1830.