Knowledge Commons of Institute of Automation,CAS
SERVE: Soft and Equalized Residual VEctors for image retrieval | |
Li, Jun1; Xu, Chang2; Gong, Mingming3; Xing, Junliang4; Yang, Wankou1; Sun, Changyin1 | |
发表期刊 | NEUROCOMPUTING |
2016-09-26 | |
卷号 | 207期号:0页码:202-212 |
文章类型 | Article |
摘要 | In 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. |
关键词 | Serve Manifolds Multi-graph Embedding Graph Ensemble Image Retrieval |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1016/j.neucom.2016.04.047 |
关键词[WOS] | OBJECT RETRIEVAL ; RELEVANCE FEEDBACK ; RE-RANKING ; REPRESENTATION ; FEATURES ; DATABASE ; VLAD |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China(61473086 ; Natural Science Foundation of Jiangsu Province(BK20140566 ; 61375001) ; BK20150470) |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000382794500018 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12647 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
作者单位 | 1.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 |
推荐引用方式 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. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
NC16SERVE.pdf(2902KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论