Supervised Topology Preserving Hashing

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	Supervised Topology Preserving Hashing
	Shu Zhang 1,2,3; Man Zhang 1,2,3; Qi Li1,2,3 ; Tieniu Tan 1,2,3; Ran He1,2,3 ; Zhang, Shu
	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
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.