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SERVE: Soft and Equalized Residual VEctors for image retrieval
Li, Jun1; Xu, Chang2; Gong, Mingming3; Xing, Junliang4; Yang, Wankou1; Sun, Changyin1
Source PublicationNEUROCOMPUTING
AbstractIn the last decade, a wide variety of image signatures, e.g., Bag-of-Visual-Words (BOVW), Fisher Vector (FV), and Vector of Locally Aggregated Descriptor (VLAD), have been developed for effective image retrieval. These image signatures, however, are either computationally expensive or simplified for the purpose of trading accuracy for efficiency. To simultaneously guarantee efficiency and effectiveness, we propose a novel image signature termed Soft and Equalized Residual VEctors (SERVE) which is more discriminatively formulated and maintains higher accuracy. It improves VLAD by encoding the variability in within-cluster feature points into the summation of Residual Vectors.(RV) while manifesting superiority in computational efficiency over FV. To find the latent low-dimensional manifolds underlying in the SERVE feature space, we propose to partition the original feature space into separate subspaces by random projections and employ multi-graph embedding to obtain additional performance gain. In particular, we make use of two fusion strategies for graph ensemble to generate a holistic representation. Extensive empirical studies carried out on the three retrieval-specific public benchmarks reveal that our method outperforms existing state-of-the-art methods and provides a promising paradigm for the image retrieval task. (C) 2016 Published by Elsevier B.V.
KeywordServe Manifolds Multi-graph Embedding Graph Ensemble Image Retrieval
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61473086 ; Natural Science Foundation of Jiangsu Province(BK20140566 ; 61375001) ; BK20150470)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000382794500018
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Document Type期刊论文
Affiliation1.Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China
2.Peking Univ, Sch Elect Engn & Comp Sci, Minist Educ, Key Lab Machine Percept, Beijing 100871, Peoples R China
3.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Jun,Xu, Chang,Gong, Mingming,et al. SERVE: Soft and Equalized Residual VEctors for image retrieval[J]. NEUROCOMPUTING,2016,207(0):202-212.
APA Li, Jun,Xu, Chang,Gong, Mingming,Xing, Junliang,Yang, Wankou,&Sun, Changyin.(2016).SERVE: Soft and Equalized Residual VEctors for image retrieval.NEUROCOMPUTING,207(0),202-212.
MLA Li, Jun,et al."SERVE: Soft and Equalized Residual VEctors for image retrieval".NEUROCOMPUTING 207.0(2016):202-212.
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