Online Multi-Modal Distance Metric Learning with Application to Image Retrieval

doi:10.1109/TKDE.2015.2477296

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Online Multi-Modal Distance Metric Learning with Application to Image Retrieval
	Wu, Pengcheng 1; Hoi, Steven C. H.1; Zhao, Peilin 2; Miao, Chunyan 3; Liu, Zhi-Yong4
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
	2016-02-01
卷号	28 期号:2 页码:454-467
文章类型	Article
摘要	Distance metric learning (DML) is an important technique to improve similarity search in content-based image retrieval. Despite being studied extensively, most existing DML approaches typically adopt a single-modal learning framework that learns the distance metric on either a single feature type or a combined feature space where multiple types of features are simply concatenated. Such single-modal DML methods suffer from some critical limitations: (i) some type of features may significantly dominate the others in the DML task due to diverse feature representations; and (ii) learning a distance metric on the combined high-dimensional feature space can be extremely time-consuming using the naive feature concatenation approach. To address these limitations, in this paper, we investigate a novel scheme of online multi-modal distance metric learning (OMDML), which explores a unified two-level online learning scheme: (i) it learns to optimize a distance metric on each individual feature space; and (ii) then it learns to find the optimal combination of diverse types of features. To further reduce the expensive cost of DML on high-dimensional feature space, we propose a low-rank OMDML algorithm which not only significantly reduces the computational cost but also retains highly competing or even better learning accuracy. We conduct extensive experiments to evaluate the performance of the proposed algorithms for multi-modal image retrieval, in which encouraging results validate the effectiveness of the proposed technique.
关键词	Content-based Image Retrieval Multi-modal Retrieval Distance Metric Learning Online Learning
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TKDE.2015.2477296
关键词[WOS]	CLASSIFICATION ; ALGORITHMS ; SHAPE
收录类别	SCI
语种	英语
项目资助者	Singapore Ministry of Education(14-C220-SMU-016)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000369006800013
引用统计	被引频次：60[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11333
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
作者单位	1.Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore 2.ASTAR, Data Analyt Dept, Inst Infocomm Res, Singapore 138632, Singapore 3.Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wu, Pengcheng,Hoi, Steven C. H.,Zhao, Peilin,et al. Online Multi-Modal Distance Metric Learning with Application to Image Retrieval[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2016,28(2):454-467.
APA	Wu, Pengcheng,Hoi, Steven C. H.,Zhao, Peilin,Miao, Chunyan,&Liu, Zhi-Yong.(2016).Online Multi-Modal Distance Metric Learning with Application to Image Retrieval.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,28(2),454-467.
MLA	Wu, Pengcheng,et al."Online Multi-Modal Distance Metric Learning with Application to Image Retrieval".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 28.2(2016):454-467.