We investigate the problem of automatically determining what type of shoe left an impression found at a crime scene. This recognition problem is made difficult by the variability in types of crime scene evidence (ranging from traces of dust or oil on hard surfaces to impressions made in soil) and the lack of comprehensive databases of shoe outsole tread patterns. We find that mid-level features extracted by pre-trained convolutional neural nets are surprisingly effective descriptors for this specialized domains. However, the choice of similarity measure for matching exemplars to a query image is essential to good performance. For matching multi-channel deep features, we propose the use of multi-channel normalized cross-correlation and analyze its effectiveness. Our proposed metric significantly improves performance in matching crime scene shoeprints to laboratory test impressions. We also show its effectiveness in other cross-domain image retrieval problems: matching facade images to segmentation labels and aerial photos to map images. Finally, we introduce a discriminatively trained variant and fine-tune our system through our proposed metric, obtaining state-of-the-art performance.

中文翻译：

---

具有深度特征图的跨域图像匹配

我们调查了自动确定在犯罪现场发现的鞋子留下印象的问题。由于犯罪现场证据类型的可变性（从硬表面上的灰尘或油迹到土壤中留下的痕迹）以及缺乏鞋外底胎面花纹的综合数据库，识别问题变得困难。我们发现由预训练的卷积神经网络提取的中级特征对于这个专业领域来说是非常有效的描述符。然而，用于将样本与查询图像匹配的相似性度量的选择对于良好的性能至关重要。为了匹配多通道深度特征，我们建议使用多通道归一化互相关并分析其有效性。我们提出的指标显着提高了将犯罪现场鞋印与实验室测试印象相匹配的性能。我们还展示了它在其他跨域图像检索问题中的有效性：将立面图像与分割标签相匹配，将航拍照片与地图图像相匹配。最后，我们引入了一个经过区分训练的变体，并通过我们提出的指标微调我们的系统，获得最先进的性能。