Weakly Supervised Multi-Graph Learning for Robust Image Reranking
文献类型：期刊论文
| 作者 | Deng, Cheng1; Ji, Rongrong2; Tao, Dacheng3 ; Gao, Xinbo1; Li, Xuelong4
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| 刊名 | ieee transactions on multimedia
 |
| 出版日期 | 2014-04-01 |
| 卷号 | 16期号:3页码:785-795 |
| 关键词 | Attributes co-occurred patterns multiple graphs visual reranking weakly supervised learning |
| ISSN号 | 1520-9210 |
| 英文摘要 | visual reranking has been widely deployed to refine the traditional text-based image retrieval. its current trend is to combine the retrieval results from various visual features to boost reranking precision and scalability. and its prominent challenge is how to effectively exploit the complementary property of different features. another significant issue raises from the noisy instances, from manual or automatic labels, which makes the exploration of such complementary property difficult. this paper proposes a novel image reranking by introducing a new co-regularized multigraph learning (co-rmgl) framework, in which intra-graph and inter-graph constraints are integrated to simultaneously encode the similarity in a single graph and the consistency across multiple graphs. to deal with the noisy instances, weakly supervised learning via co-occurred visual attribute is utilized to select a set of graph anchors to guide multiple graphs alignment and fusion, and to filter out those pseudo labeling instances to highlight the strength of individual features. after that, a learned edge weighting matrix from a fused graph is used to reorder the retrieval results. we evaluate our approach on four popular image retrieval data sets and demonstrate a significant improvement over state-of-the-art methods. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, information systems ; computer science, software engineering ; telecommunications |
| 研究领域[WOS] | computer science ; telecommunications |
| 关键词[WOS] | visual-search ; recognition ; ranking ; models |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000333111500018 |
| 公开日期 | 2015-03-18 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/22382]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China 2.Xiamen Univ, Sch Informat Sci & Technol, Dept Cognit Sci, Xiamen 31005, Fujian, Peoples R China 3.Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Broadway, NSW 2007, Australia 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OP TIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Deng, Cheng,Ji, Rongrong,Tao, Dacheng,et al. Weakly Supervised Multi-Graph Learning for Robust Image Reranking[J]. ieee transactions on multimedia,2014,16(3):785-795. |
| APA | Deng, Cheng,Ji, Rongrong,Tao, Dacheng,Gao, Xinbo,&Li, Xuelong.(2014).Weakly Supervised Multi-Graph Learning for Robust Image Reranking.ieee transactions on multimedia,16(3),785-795. |
| MLA | Deng, Cheng,et al."Weakly Supervised Multi-Graph Learning for Robust Image Reranking".ieee transactions on multimedia 16.3(2014):785-795. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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