{"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/vlase-vehicle-localization-by-aggregating","title":"VLASE: Vehicle Localization by Aggregating Semantic Edges","arxiv_id":"1807.02536","date":"2018-07-06","proceeding":null,"authors":["Xin Yu","Sagar Chaturvedi","Chen Feng","Yuichi Taguchi","Teng-Yok Lee","Clinton Fernandes","Srikumar Ramalingam"],"abstract":"In this paper, we propose VLASE, a framework to use semantic edge features\nfrom images to achieve on-road localization. Semantic edge features denote edge\ncontours that separate pairs of distinct objects such as building-sky, road-\nsidewalk, and building-ground. While prior work has shown promising results by\nutilizing the boundary between prominent classes such as sky and building using\nskylines, we generalize this approach to consider semantic edge features that\narise from 19 different classes. Our localization algorithm is simple, yet very\npowerful. We extract semantic edge features using a recently introduced CASENet\narchitecture and utilize VLAD framework to perform image retrieval. Our\nexperiments show that we achieve improvement over some of the state-of-the-art\nlocalization algorithms such as SIFT-VLAD and its deep variant NetVLAD. We use\nablation study to study the importance of different semantic classes and show\nthat our unified approach achieves better performance compared to individual\nprominent features such as skylines.","url_abs":"http://arxiv.org/abs/1807.02536v1","url_pdf":"http://arxiv.org/pdf/1807.02536v1.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":"vlase-vehicle-localization-by-aggregating","repo_url":"https://github.com/sagachat/VLASE","is_official":0,"mentioned_in_paper":0,"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=1807.02536","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}