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A large number of criminal investigation images are collected for modern case detection, and these images not only
contain many valuable clues, but also can provide powerful evidence. At present, most of the current criminal
investigation image retrieval methods for public security investigation applications use text-based or traditional shallow
feature-based image retrieval methods, and the accuracy and efficiency of retrieval can hardly meet the needs of modern
criminal investigation cases. In this paper, we summarize and analyze the current research results and technologies in the
field of image retrieval, and adopt the image retrieval method based on depth features to improve the accuracy and
efficiency of the criminal investigation present investigation image retrieval. In the depth feature-based image retrieval,
VGGNet and Res Net are fine-tuned using the criminal investigation image dataset, and then the image depth features
are extracted for retrieval experiments. The experimental results show that the retrieval model has the following two
shortcomings: the model cannot adapt to the target scale change; the retrieval accuracy is lower than the average in the
category with fewer samples. Two optimizations are proposed to address these problems: introducing pyramid pooling to
improve the robustness of the model to target scale changes; retraining the network after enhancing the data samples to
make the retrieval accuracy of the model more balanced for different categories of samples. In addition, query expansion
is introduced to enhance the image feature representation during retrieval. After using the above optimization methods,
the retrieval accuracy is improved by 5.7%.
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