{"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/fast-supervised-hashing-with-decision-trees","title":"Fast Supervised Hashing with Decision Trees for High-Dimensional Data","arxiv_id":"1404.1561","date":"2014-04-06","proceeding":"CVPR 2014 6","authors":["Guosheng Lin","Chunhua Shen","Qinfeng Shi","Anton Van Den Hengel","David Suter"],"abstract":"Supervised hashing aims to map the original features to compact binary codes\nthat are able to preserve label based similarity in the Hamming space.\nNon-linear hash functions have demonstrated the advantage over linear ones due\nto their powerful generalization capability. In the literature, kernel\nfunctions are typically used to achieve non-linearity in hashing, which achieve\nencouraging retrieval performance at the price of slow evaluation and training\ntime. Here we propose to use boosted decision trees for achieving non-linearity\nin hashing, which are fast to train and evaluate, hence more suitable for\nhashing with high dimensional data. In our approach, we first propose\nsub-modular formulations for the hashing binary code inference problem and an\nefficient GraphCut based block search method for solving large-scale inference.\nThen we learn hash functions by training boosted decision trees to fit the\nbinary codes. Experiments demonstrate that our proposed method significantly\noutperforms most state-of-the-art methods in retrieval precision and training\ntime. Especially for high-dimensional data, our method is orders of magnitude\nfaster than many methods in terms of training time.","url_abs":"http://arxiv.org/abs/1404.1561v2","url_pdf":"http://arxiv.org/pdf/1404.1561v2.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":"fast-supervised-hashing-with-decision-trees","repo_url":"https://bitbucket.org/chhshen/fasthash","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1404.1561","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}