CASIA OpenIR  > 智能感知与计算研究中心
Supervised Topology Preserving Hashing
Shu Zhang1,2,3; Man Zhang1,2,3; Qi Li1,2,3; Tieniu Tan1,2,3; Ran He1,2,3; Zhang, Shu
2015-11
会议名称Asian Conference on Pattern Recognition(ACPR)
会议录名称Asian Conference on Pattern Recognition
会议日期2015年11月3-6日
会议地点Kuala Lumpur, Malaysia
摘要Learning based hashing is gaining traction in largescale retrieval systems. It aims to learn compact binary codes that can preserve semantic similarity in the hamming space. This paper presents a supervised topology hashing (SPTH) algorithm to learn compact binary codes that can exploit both the supervisory information as well as the local topology structure of datasets. To build a connection between the original space and the resultant hamming space, we minimize the quantization errors together with a classi- fication error term and a topology preserving term. A nonlinear kernel feature space is further used to improve the generalization power. An alternating iterative algorithm is developed to minimize the complex objective function that contains both continuous and discrete variables. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method on image retrieval tasks.
关键词Topology Hash
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11681
专题智能感知与计算研究中心
通讯作者Zhang, Shu
作者单位1.Center for Research on Intelligent Perception and Computing, CASIA
2.National Laboratory of Pattern Recognition, CASIA
3.Center for Excellence in Brain Science and Intelligence Technology, CAS
推荐引用方式
GB/T 7714
Shu Zhang,Man Zhang,Qi Li,et al. Supervised Topology Preserving Hashing[C],2015.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
07486510.pdf(191KB)会议论文 开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Shu Zhang]的文章
[Man Zhang]的文章
[Qi Li]的文章
百度学术
百度学术中相似的文章
[Shu Zhang]的文章
[Man Zhang]的文章
[Qi Li]的文章
必应学术
必应学术中相似的文章
[Shu Zhang]的文章
[Man Zhang]的文章
[Qi Li]的文章
相关权益政策
暂无数据
收藏/分享
文件名: 07486510.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。