{"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-discrete-hashing-and-its","title":"Fast Supervised Discrete Hashing and its Analysis","arxiv_id":"1611.10017","date":"2016-11-30","proceeding":null,"authors":["Gou Koutaki","Keiichiro Shirai","Mitsuru Ambai"],"abstract":"In this paper, we propose a learning-based supervised discrete hashing\nmethod. Binary hashing is widely used for large-scale image retrieval as well\nas video and document searches because the compact representation of binary\ncode is essential for data storage and reasonable for query searches using\nbit-operations. The recently proposed Supervised Discrete Hashing (SDH)\nefficiently solves mixed-integer programming problems by alternating\noptimization and the Discrete Cyclic Coordinate descent (DCC) method. We show\nthat the SDH model can be simplified without performance degradation based on\nsome preliminary experiments; we call the approximate model for this the \"Fast\nSDH\" (FSDH) model. We analyze the FSDH model and provide a mathematically exact\nsolution for it. In contrast to SDH, our model does not require an alternating\noptimization algorithm and does not depend on initial values. FSDH is also\neasier to implement than Iterative Quantization (ITQ). Experimental results\ninvolving a large-scale database showed that FSDH outperforms conventional SDH\nin terms of precision, recall, and computation time.","url_abs":"http://arxiv.org/abs/1611.10017v1","url_pdf":"http://arxiv.org/pdf/1611.10017v1.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-discrete-hashing-and-its","repo_url":"https://github.com/goukoutaki/FSDH","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"quantization","task_name":"Quantization"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}