Ranking Graph Embedding for Learning to Rerank
文献类型：期刊论文
| 作者 | Pang, Yanwei1; Ji, Zhong1; Jing, Peiguang1; Li, Xuelong2
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| 刊名 | ieee transactions on neural networks and learning systems
 |
| 出版日期 | 2013-08-01 |
| 卷号 | 24期号:8页码:1292-1303 |
| 关键词 | Dimensionality reduction graph embedding image search reranking learning to rank |
| 英文摘要 | dimensionality reduction is a key step to improving the generalization ability of reranking in image search. however, existing dimensionality reduction methods are typically designed for classification, clustering, and visualization, rather than for the task of learning to rank. without using of ranking information such as relevance degree labels, direct utilization of conventional dimensionality reduction methods in ranking tasks generally cannot achieve the best performance. in this paper, we show that introducing ranking information into dimensionality reduction significantly increases the performance of image search reranking. the proposed method transforms graph embedding, a general framework of dimensionality reduction, into ranking graph embedding (range) by modeling the global structure and the local relationships in and between different relevance degree sets, respectively. the proposed method also defines three types of edge weight assignment between two nodes: binary, reconstruction, and global. in addition, a novel principal components analysis based similarity calculation method is presented in the stage of global graph construction. extensive experimental results on the msra-mm database demonstrate the effectiveness and superiority of the proposed range method and the image search reranking framework. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic |
| 研究领域[WOS] | computer science ; engineering |
| 关键词[WOS] | dimensionality reduction ; relevance feedback ; video annotation ; image retrieval ; search ; framework ; subspace |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000322039500010 |
| 公开日期 | 2015-06-30 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/23427]  |
| 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.Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Ctr Opt IMagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pang, Yanwei,Ji, Zhong,Jing, Peiguang,et al. Ranking Graph Embedding for Learning to Rerank[J]. ieee transactions on neural networks and learning systems,2013,24(8):1292-1303. |
| APA | Pang, Yanwei,Ji, Zhong,Jing, Peiguang,&Li, Xuelong.(2013).Ranking Graph Embedding for Learning to Rerank.ieee transactions on neural networks and learning systems,24(8),1292-1303. |
| MLA | Pang, Yanwei,et al."Ranking Graph Embedding for Learning to Rerank".ieee transactions on neural networks and learning systems 24.8(2013):1292-1303. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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