Supervised Topology Preserving Hashing | |
Shu Zhang1,2,3; Man Zhang1,2,3; Qi Li1,2,3![]() ![]() | |
2015-11 | |
会议日期 | 2015年11月3-6日 |
会议地点 | Kuala Lumpur, Malaysia |
关键词 | Topology Hash |
英文摘要 | 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. |
会议录 | Asian Conference on Pattern Recognition
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内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/11681] ![]() |
专题 | 自动化研究所_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]. 见:. Kuala Lumpur, Malaysia. 2015年11月3-6日. |
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