{"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/beyond-outlier-removal-integrated-ensemble","title":"Beyond outlier removal: Integrated ensemble matching for accurate image keypoint correspondence","arxiv_id":null,"date":"2025-03-24","proceeding":"Knowledge-Based Systems 2025 3","authors":["Javid Norouzi","Mohammad Sadegh Helfroush","Alireza Liaghat","Habibollah Danyali"],"abstract":"This paper presents a novel two-tier matching approach for robust feature correspondence and geometric transformation estimation in computer vision tasks. Our method combines sub-descriptor matching and multi-descriptor ensembling to significantly improve the accuracy and reliability of keypoint correspondences across images. Our approach is evaluated on both classic hand-crafted algorithms and deep learning-based methods using the HPatches, IMC2022, and YFCC100M datasets. We use a pretrained network and enhance it to incorporate additional descriptor heads optimized for our matching strategy. The two-tier matching strategy enables direct geometric transformation estimation without separate outlier removal in many cases, potentially streamlining computer vision pipelines. Results demonstrate that our approach substantially outperforms traditional single-descriptor methods, achieving over 95% correct matches for classic algorithm combinations and up to 96% for our enhanced learning-based approaches. Our approach can establish reliable correspondences without making any assumption about the mathematical relation between the matches. Additionally, we explore applications of our method in scene matching.","url_abs":"https://doi.org/10.1016/j.knosys.2025.113343","url_pdf":"https://kwnsfk27.r.eu-west-1.awstrack.me/L0/https:%2F%2Fauthors.elsevier.com%2Fa%2F1kpdI3OAb9GnA5/1/01020195c926fc33-d20c1e40-b42c-42c5-9215-8c524c6eab27-000000/bQ3m5k1bjamTeB_NMxewdcaNniw=418","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":"beyond-outlier-removal-integrated-ensemble","repo_url":"https://github.com/ShowStopperTheSecond/MatchBeyondOutliers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}