Online Multi-Modal Distance Metric Learning with Application to Image Retrieval
Wu, Pengcheng1; Hoi, Steven C. H.1; Zhao, Peilin2; Miao, Chunyan3; Liu, Zhi-Yong4
Source PublicationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
2016-02-01
Volume28Issue:2Pages:454-467
SubtypeArticle
AbstractDistance 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.
KeywordContent-based Image Retrieval Multi-modal Retrieval Distance Metric Learning Online Learning
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TKDE.2015.2477296
WOS KeywordCLASSIFICATION ; ALGORITHMS ; SHAPE
Indexed BySCI
Language英语
Funding OrganizationSingapore Ministry of Education(14-C220-SMU-016)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000369006800013
Citation statistics
Cited Times:20[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11333
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Affiliation1.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
Recommended Citation
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.
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