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
Conference NameAsian Conference on Pattern Recognition(ACPR)
Source PublicationAsian Conference on Pattern Recognition
Conference Date2015年11月3-6日
Conference PlaceKuala Lumpur, Malaysia
AbstractLearning 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.
KeywordTopology Hash
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11681
Collection智能感知与计算研究中心
Corresponding AuthorZhang, Shu
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shu Zhang,Man Zhang,Qi Li,et al. Supervised Topology Preserving Hashing[C],2015.
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