{"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/efficient-nearest-neighbors-search-for-large","title":"Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition","arxiv_id":"1806.05946","date":"2018-06-15","proceeding":null,"authors":["Federico Magliani","Tomaso Fontanini","Andrea Prati"],"abstract":"The problem of landmark recognition has achieved excellent results in\nsmall-scale datasets. When dealing with large-scale retrieval, issues that were\nirrelevant with small amount of data, quickly become fundamental for an\nefficient retrieval phase. In particular, computational time needs to be kept\nas low as possible, whilst the retrieval accuracy has to be preserved as much\nas possible. In this paper we propose a novel multi-index hashing method called\nBag of Indexes (BoI) for Approximate Nearest Neighbors (ANN) search. It allows\nto drastically reduce the query time and outperforms the accuracy results\ncompared to the state-of-the-art methods for large-scale landmark recognition.\nIt has been demonstrated that this family of algorithms can be applied on\ndifferent embedding techniques like VLAD and R-MAC obtaining excellent results\nin very short times on different public datasets: Holidays+Flickr1M, Oxford105k\nand Paris106k.","url_abs":"http://arxiv.org/abs/1806.05946v1","url_pdf":"http://arxiv.org/pdf/1806.05946v1.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":"efficient-nearest-neighbors-search-for-large","repo_url":"https://github.com/fmaglia/BoI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"landmark-recognition","task_name":"Landmark Recognition"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}