Spatial Pyramid Covariance-Based Compact Video Code for Robust Face Retrieval in TV-Series

doi:10.1109/TIP.2016.2616297

	Spatial Pyramid Covariance-Based Compact Video Code for Robust Face Retrieval in TV-Series
	Li, Yan 1,2; Wang, Ruiping 1,2; Cui, Zhen 3; Shan, Shiguang 1,2,4; Chen, Xilin 1,2
	2016-12-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	25 期号:12 页码:5905-5919
摘要	We address the problem of face video retrieval in TV-series, which searches video clips based on the presence of specific character, given one face track of his/her. This is tremendously challenging because on one hand, faces in TV-series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand, retrieval task typically needs efficient representation with low time and space complexity. To handle this problem, we propose a compact and discriminative representation for the huge body of video data, named compact video code (CVC). Our method first models the face track by its sample (i.e., frame) covariance matrix to capture the video data variations in a statistical manner. To incorporate discriminative information and obtain more compact video signature suitable for retrieval, the high-dimensional covariance representation is further encoded as a much lower dimensional binary vector, which finally yields the proposed CVC. Specifically, each bit of the code, i.e., each dimension of the binary vector, is produced via supervised learning in a max margin framework, which aims to make a balance between the discriminability and stability of the code. Besides, we further extend the descriptive granularity of covariance matrix from traditional pixel-level to more general patch-level, and proceed to propose a novel hierarchical video representation named spatial pyramid covariance along with a fast calculation method. Face retrieval experiments on two challenging TV-series video databases, i.e., the Big Bang Theory and Prison Break, demonstrate the competitiveness of the proposed CVC over the state-of-the-art retrieval methods. In addition, as a general video matching algorithm, CVC is also evaluated in traditional video face recognition task on a [GRAPHICS] standard Internet database, i.e., YouTube Celebrities, showing its quite promising performance by using an extremely compact code with only 128 bits.
关键词	Face video retrieval covariance matrix spatial pyramid covariance compact video code binary code learning
DOI	10.1109/TIP.2016.2616297
收录类别	SCI
语种	英语
资助项目	973 Program[2015CB351802] ; Natural Science Foundation of China[61390511] ; Natural Science Foundation of China[61222211] ; Natural Science Foundation of China[61379083] ; Natural Science Foundation of China[61271445] ; Strategic Priority Research Program of CAS[XDB02070004] ; Youth Innovation Promotion Association CAS[2015085] ; Natural Science Foundation of Zhejiang Province, China[LQ15F020005]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000388205200015
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7950
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shan, Shiguang
作者单位	1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Southeast Univ, Nanjing 210096, Jiangsu, Peoples R China 4.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
推荐引用方式 GB/T 7714	Li, Yan,Wang, Ruiping,Cui, Zhen,et al. Spatial Pyramid Covariance-Based Compact Video Code for Robust Face Retrieval in TV-Series[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(12):5905-5919.
APA	Li, Yan,Wang, Ruiping,Cui, Zhen,Shan, Shiguang,&Chen, Xilin.(2016).Spatial Pyramid Covariance-Based Compact Video Code for Robust Face Retrieval in TV-Series.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(12),5905-5919.
MLA	Li, Yan,et al."Spatial Pyramid Covariance-Based Compact Video Code for Robust Face Retrieval in TV-Series".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.12(2016):5905-5919.