Abstract
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 mainstream method of CSI image feature extraction and representation,and in this process,datasets provide effective support for CSI retrieval performance.However,there is a lack of systematic research on CSI image retrieval methods and datasets.Therefore,we present an overview of the existing works about one-class and multi-class CSI image retrieval based on deep learning.According to the research,based on their technical functionalities and implementation methods,CSI image retrieval is 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.
基金项目
National Natural Science Foundation of China(62301423)
Special Scientific Research Plan Project of Shaanxi Provincial Department of Education(23JK0671)
NETL
NSTL
万方数据