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Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning

文献类型：期刊论文

作者Han, Hu1,2,3; Li, Jie2,3; Jain, Anil K.4; Shan, Shiguang2,3,5; Chen, Xilin2,3
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2019-10-01
卷号41期号:10页码:2333-2348
ISSN号0162-8828
关键词Large-scale tattoo search joint detection and representation learning sketch based search multi-task learning
DOI10.1109/TPAMI.2019.2891584
英文摘要The explosive growth of digital images in video surveillance and social media has led to the significant need for efficient search of persons of interest in law enforcement and forensic applications. Despite tremendous progress in primary biometric traits (e.g., face and fingerprint) based person identification, a single biometric trait alone can not meet the desired recognition accuracy in forensic scenarios. Tattoos, as one of the important soft biometric traits, have been found to be valuable for assisting in person identification. However, tattoo search in a large collection of unconstrained images remains a difficult problem, and existing tattoo search methods mainly focus on matching cropped tattoos, which is different from real application scenarios. To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning. While the features in the backbone network are shared by both tattoo detection and compact representation learning, individual latent layers of each sub-network optimize the shared features toward the detection and feature learning tasks, respectively. We resolve the small batch size issue inside the joint tattoo detection and compact representation learning network via random image stitch and preceding feature buffering. We evaluate the proposed tattoo search system using multiple public-domain tattoo benchmarks, and a gallery set with about 300K distracter tattoo images compiled from these datasets and images from the Internet. In addition, we also introduce a tattoo sketch dataset containing 300 tattoos for sketch-based tattoo search. Experimental results show that the proposed approach has superior performance in tattoo detection and tattoo search at scale compared to several state-of-the-art tattoo retrieval algorithms.
资助项目Natural Science Foundation of China[61732004] ; Natural Science Foundation of China[61672496] ; Natural Science Foundation of China[61650202] ; Strategic Priority Research Program of CAS[XDB02070004] ; External Cooperation Program of CAS[GJHZ1843]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:000489763000005
源URL[http://119.78.100.204/handle/2XEOYT63/4624]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Han, Hu
作者单位1.Peng Cheng Lab, Shenzhen, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
4.Michigan State Univ, Dept Comp Sci & Engn, E Lansing, MI 48824 USA
5.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Han, Hu,Li, Jie,Jain, Anil K.,et al. Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2019,41(10):2333-2348.
APA Han, Hu,Li, Jie,Jain, Anil K.,Shan, Shiguang,&Chen, Xilin.(2019).Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,41(10),2333-2348.
MLA Han, Hu,et al."Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 41.10(2019):2333-2348.

入库方式： OAI收割

来源：计算技术研究所

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