Knowledge Commons of Institute of Automation,CAS
K-nearest neighbors hashing | |
He, Xiangyu1,2![]() ![]() ![]() | |
2019-04 | |
会议名称 | IEEE Conference on Computer Vision and Pattern Recognition |
会议日期 | 2019-4-12 |
会议地点 | Long Beach, CA |
摘要 | Hashing based approximate nearest neighbor search embeds high dimensional data to compact binary codes, which enables efficient similarity search and storage. However, the non-isometry sign () function makes it hard to project the nearest neighbors in continuous data space into the closest codewords in discrete Hamming space. In this work, we revisit the sign () function from the perspective of space partitioning. In specific, we bridge the gap between k-nearest neighbors and binary hashing codes with Shannon entropy. We further propose a novel K-Nearest Neighbors Hashing (KNNH) method to learn binary representations from KNN within the subspaces generated by sign (). Theoretical and experimental results show that the KNN relation is of central importance to neighbor preserving embeddings, and the proposed method outperforms the state-of-the-arts on benchmark datasets. |
收录类别 | EI |
七大方向——子方向分类 | AI芯片与智能计算 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40623 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
通讯作者 | Cheng, Jian |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | He, Xiangyu,Wang, Peisong,Cheng, Jian. K-nearest neighbors hashing[C],2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
K-Nearest_Neighbors_(344KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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