Ranking Graph Embedding for Learning to Rerank

doi:10.1109/TNNLS.2013.2253798

OPT OpenIR > 光谱成像技术研究室

	Ranking Graph Embedding for Learning to Rerank
	Pang, Yanwei 1; Ji, Zhong 1; Jing, Peiguang 1; Li, Xuelong2
	2013-08-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号	24 期号:8 页码:1292-1303
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.
文章类型	Article
关键词	Dimensionality Reduction Graph Embedding Image Search Reranking Learning To Rank
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TNNLS.2013.2253798
收录类别	SCI ; EI
关键词[WOS]	DIMENSIONALITY REDUCTION ; RELEVANCE FEEDBACK ; VIDEO ANNOTATION ; IMAGE RETRIEVAL ; SEARCH ; FRAMEWORK ; SUBSPACE
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000322039500010
引用统计	被引频次：52[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/23427
专题	光谱成像技术研究室
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.

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