A Survey of Crime Scene Investigation Image Retrieval Using Deep Learning  

作　　者：Ying Liu Aodong Zhou Jize Xue Zhijie Xu 

机构地区：[1]Xi’an University of Posts and Telecommunications(XUPT),Xi’an 710121,China [2]University of Huddersfield,Huddersfield HD13DH,UK

出　　处：《Journal of Beijing Institute of Technology》2024年第4期271-286,共16页北京理工大学学报（英文版）

摘　　要：Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.

关 键 词：crime scene investigation(CSI)image image retrieval deep learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术] D918.2[政治法律—法学]