| Weakly Supervised Multi-Graph Learning for Robust Image Reranking | |
Deng, Cheng1; Ji, Rongrong2; Tao, Dacheng3 ; Gao, Xinbo1; Li, Xuelong4
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| 作者部门 | 光学影像学习与分析中心 |
| 2014-04-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON MULTIMEDIA
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| ISSN | 1520-9210 |
| 卷号 | 16期号:3页码:785-795 |
| 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. |
| 文章类型 | Article |
| 关键词 | Attributes Co-occurred Patterns Multiple Graphs Visual Reranking Weakly Supervised Learning |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TMM.2014.2298841 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | VISUAL-SEARCH ; RECOGNITION ; RANKING ; MODELS |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Telecommunications |
| WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
| WOS记录号 | WOS:000333111500018 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/22382 |
| 专题 | 光谱成像技术研究室 |
| 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. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Weakly Supervised Mu（2085KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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