1. 西安邮电大学图像与信息处理研究所,陕西,西安,710121
2. 电子信息现场勘验应用技术公安部重点实验室,陕西,西安,710121
网络出版:2018-03-25,
纸质出版:2018
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刘颖, 胡丹, 范九伦. 现勘图像检索综述[J]. 电子学报, 2018,46(3):761-768.
LIU Ying, HU Dan, FAN Jiu-lun. A Survey of Crime Scene Investigation Image Retrieval[J]. Acta Electronica Sinica, 2018, 46(3): 761-768.
刘颖, 胡丹, 范九伦. 现勘图像检索综述[J]. 电子学报, 2018,46(3):761-768. DOI: 10.3969/j.issn.0372-2112.2018.03.035.
LIU Ying, HU Dan, FAN Jiu-lun. A Survey of Crime Scene Investigation Image Retrieval[J]. Acta Electronica Sinica, 2018, 46(3): 761-768. DOI: 10.3969/j.issn.0372-2112.2018.03.035.
现勘图像检索是进行证据图像比对以获取物证信息的重要手段.本文基于目前应用广泛的现勘图像数据库,根据图像内容将图像分为鞋印、指纹、纹身等种类.并通过对现勘图像的两项关键技术即低层数字特征提取和高层语义分析的总结,从颜色特征、纹理特征、边缘提取等方面综述了现勘图像低层数字特征提取技术,从利用语义模板和数据库本体结构、机器学习算法、引入人工反馈三大类高层语义提取技术综述了现勘图像高层语义分析的研究成果.最后,结合公安行业利用现勘图像获取物证线索的实际应用需求,指出了通过引入公安行业先验知识来提高检索效率等研究方向.
Crime scene investigation (CSI) image retrieval is an important means to obtain material evidence for case solving. This paper describes the CSI image datasets
which are classified into different categories according to the content of the data
such as shoe marks
finger prints
tattoo
etc. This paper provides a survey on state-of-the-art techniques in CSI image retrieval focusing on low-level feature extraction and high-level semantic learning. Low-level CSI image features mainly include color feature
texture feature
boundary descriptor
etc. And
three categories of high-level semantic extraction techniques for CSI images are identified including using semantic template and database ontology
machine learning techniques and introducing relevance feedback. In addition
based on practical requirements from the police on using CSI images to find evidence clues
a few research directions are suggested such as introducing prior knowledge of the police to enhance retrieval efficiency.
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