A Survey of Crime Scene Investigation Image Retrieval Using Deep Learning

Abstract

Crime scene investigation (CSI) image is key evidence carrier during criminal investigation, 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 adversarial networks, autoencoder networks and attention networks. Furthermore, We analyzed the remaining challenges and discussed future work directions in this field.

Keywords

crime scene investigation (CSI) image / image retrieval / deep learning