{"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/comparative-evaluation-of-2d-feature","title":"Comparative evaluation of 2D feature correspondence selection algorithms","arxiv_id":"1904.13383","date":"2019-04-30","proceeding":null,"authors":["Chen Zhao","Jiaqi Yang","Yang Xiao","Zhiguo Cao"],"abstract":"Correspondence selection aiming at seeking correct feature correspondences\nfrom raw feature matches is pivotal for a number of feature-matching-based\ntasks. Various 2D (image) correspondence selection algorithms have been\npresented with decades of progress. Unfortunately, the lack of an in-depth\nevaluation makes it difficult for developers to choose a proper algorithm given\na specific application. This paper fills this gap by evaluating eight 2D\ncorrespondence selection algorithms ranging from classical methods to the most\nrecent ones on four standard datasets. The diversity of experimental datasets\nbrings various nuisances including zoom, rotation, blur, viewpoint change, JPEG\ncompression, light change, different rendering styles and multi-structures for\ncomprehensive test. To further create different distributions of initial\nmatches, a set of combinations of detector and descriptor is also taken into\nconsideration. We measure the quality of a correspondence selection algorithm\nfrom four perspectives, i.e., precision, recall, F-measure and efficiency.\nAccording to evaluation results, the current advantages and limitations of all\nconsidered algorithms are aggregately summarized which could be treated as a\n\"user guide\" for the following developers.","url_abs":"http://arxiv.org/abs/1904.13383v1","url_pdf":"http://arxiv.org/pdf/1904.13383v1.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":"comparative-evaluation-of-2d-feature","repo_url":"https://github.com/izhangrui/paper_to_read","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}