Knowledge Commons of Institute of Automation,CAS
Random subspace for binary codes learning in large scale image retrieval | |
Leng, Cong; Cheng, Jian; Lu, Hanqing; Jian Cheng | |
2014 | |
会议名称 | SIGIR 2014 - the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval |
会议录名称 | International ACM SIGIR Conference on Research and Development in Information Retrieval |
页码 | 1031-1034 |
会议日期 | 2014 |
会议地点 | Australia |
摘要 |
Due to the fast query speed and low storage cost, hashing based approximate nearest neighbor search methods have attracted much attention recently. Many state of the art
methods are based on eigenvalue decomposition. In these approaches, the information caught in different dimensions is unbalanced and generally most of the information is contained in the top eigenvectors. We demonstrate that this leads to an unexpected phenomenon that longer hashing code does not necessarily yield better performance. In this work, we introduce a random subspace strategy to address this limitation. At first, a small fraction of the whole feature space is randomly sampled to train the hashing algorithms each time and only the top eigenvectors are kept to generate one piece of short code. This process will be repeated several times and then the obtained many pieces of short codes are concatenated into one piece of long code. Theoretical analysis and experiments on two benchmarks confirm the effectiveness of the proposed strategy for hashing. |
关键词 | Image Retrieval Random Subspace Binary Codes Hamming Ranking |
收录类别 | EI |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/4676 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
通讯作者 | Jian Cheng |
作者单位 | 中科院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Leng, Cong,Cheng, Jian,Lu, Hanqing,et al. Random subspace for binary codes learning in large scale image retrieval[C],2014:1031-1034. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
SIGIR2014_Random Sub(552KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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