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Parametric local multiview hamming distance metric learning

文献类型：期刊论文

作者Zhai, Deming3; Liu, Xianming3; Chang, Hong1; Zhen, Yi5; Chen, Xilin1; Guo, Maozu4; Gao, Wen2,3
刊名PATTERN RECOGNITION
出版日期2018-03-01
卷号75页码:250-262
ISSN号0031-3203
关键词Metric learning Hamming distance Hash function learning
DOI10.1016/j.patcog.2017.06.018
英文摘要Learning an appropriate distance metric is a crucial problem in pattern recognition. To confront with the scalability issue of massive data, hamming distance on binary codes is advocated since it permits exact sub-linear kNN search and meanwhile shares the advantage of efficient storage. In this paper, we study hamming metric learning in the context of multimodal data for cross-view similarity search. We present a new method called Parametric Local Multiview Hamming metric (PLMH), which learns multiview metric based on a set of local hash functions to locally adapt to the data structure of each modality. To balance locality and computational efficiency, the hash projection matrix of each instance is parameterized, with guaranteed approximation error bound, as a linear combination of basis hash projections associated with a small set of anchor points. The weak-supervisory information (side information) provided by pair wise and triplet constraints are incorporated in a coherent way to achieve semantically effective hash codes. A local optimal conjugate gradient algorithm with orthogonal rotations is designed to learn the hash functions for each bit, and the overall hash codes are learned in a sequential manner to progressively minimize the bias. Experimental evaluations on cross-media retrieval tasks demonstrate that PLMH performs competitively against the state-of-the-art methods. (C) 2017 Elsevier Ltd. All rights reserved.
资助项目Natural Science Foundation of China[61502122] ; Natural Science Foundation of China[61672193] ; Natural Science Foundation of China[61671188] ; Natural Science Foundation of China[61571164] ; Fundamental Research Funds for the Central Universities[HIT.NSRIF.201653]
WOS研究方向Computer Science ; Engineering
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000418971900023
源URL[http://119.78.100.204/handle/2XEOYT63/6320]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Liu, Xianming
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100080, Peoples R China
2.Peking Univ, Sch Elect Engn & Comp Sci, Natl Engn Lab Video Technol, Beijing 100871, Peoples R China
3.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China
4.Beijing Univ Civil Engn & Architecture, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
5.Georgia Inst Technol, Coll Comp, Atlanta, GA 30313 USA
推荐引用方式
GB/T 7714		 Zhai, Deming,Liu, Xianming,Chang, Hong,et al. Parametric local multiview hamming distance metric learning[J]. PATTERN RECOGNITION,2018,75:250-262.
APA Zhai, Deming.,Liu, Xianming.,Chang, Hong.,Zhen, Yi.,Chen, Xilin.,...&Gao, Wen.(2018).Parametric local multiview hamming distance metric learning.PATTERN RECOGNITION,75,250-262.
MLA Zhai, Deming,et al."Parametric local multiview hamming distance metric learning".PATTERN RECOGNITION 75(2018):250-262.

入库方式： OAI收割

来源：计算技术研究所

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