{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cross-domain-image-matching-with-deep-feature","title":"Cross-Domain Image Matching with Deep Feature Maps","arxiv_id":"1804.02367","date":"2018-04-06","proceeding":null,"authors":["Bailey Kong","James Supancic","Deva Ramanan","Charless C. Fowlkes"],"abstract":"We investigate the problem of automatically determining what type of shoe\nleft an impression found at a crime scene. This recognition problem is made\ndifficult by the variability in types of crime scene evidence (ranging from\ntraces of dust or oil on hard surfaces to impressions made in soil) and the\nlack of comprehensive databases of shoe outsole tread patterns. We find that\nmid-level features extracted by pre-trained convolutional neural nets are\nsurprisingly effective descriptors for this specialized domains. However, the\nchoice of similarity measure for matching exemplars to a query image is\nessential to good performance. For matching multi-channel deep features, we\npropose the use of multi-channel normalized cross-correlation and analyze its\neffectiveness. Our proposed metric significantly improves performance in\nmatching crime scene shoeprints to laboratory test impressions. We also show\nits effectiveness in other cross-domain image retrieval problems: matching\nfacade images to segmentation labels and aerial photos to map images. Finally,\nwe introduce a discriminatively trained variant and fine-tune our system\nthrough our proposed metric, obtaining state-of-the-art performance.","url_abs":"http://arxiv.org/abs/1804.02367v2","url_pdf":"http://arxiv.org/pdf/1804.02367v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cross-domain-image-matching-with-deep-feature","repo_url":"https://github.com/bkong/MCNCC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.02367","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}