Relevance Preserving Projection and Ranking for Web Image Search Reranking

doi:10.1109/TIP.2015.2437198

OPT OpenIR > 光谱成像技术研究室

	Relevance Preserving Projection and Ranking for Web Image Search Reranking
	Ji, Zhong 1; Pang, Yanwei 1; Li, Xuelong2
	2015-11-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
卷号	24 期号:11 页码:4137-4147
摘要	An image search reranking (ISR) technique aims at refining text-based search results by mining images' visual content. Feature extraction and ranking function design are two key steps in ISR. Inspired by the idea of hypersphere in one-class classification, this paper proposes a feature extraction algorithm named hypersphere-based relevance preserving projection (HRPP) and a ranking function called hypersphere-based rank (H-Rank). Specifically, an HRPP is a spectral embedding algorithm to transform an original high-dimensional feature space into an intrinsically low-dimensional hypersphere space by preserving the manifold structure and a relevance relationship among the images. An H-Rank is a simple but effective ranking algorithm to sort the images by their distances to the hypersphere center. Moreover, to capture the user's intent with minimum human interaction, a reversed k-nearest neighbor (KNN) algorithm is proposed, which harvests enough pseudorelevant images by requiring that the user gives only one click on the initially searched images. The HRPP method with reversed KNN is named one-click-based HRPP (OC-HRPP). Finally, an OC-HRPP algorithm and the H-Rank algorithm form a new ISR method, H-reranking. Extensive experimental results on three large real-world data sets show that the proposed algorithms are effective. Moreover, the fact that only one relevant image is required to be labeled makes it has a strong practical significance.
文章类型	Article
关键词	Multimedia Information System Multimedia Ranking Feature Embedding One-class Classification Image Search Reranking
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TIP.2015.2437198
收录类别	SCI ; EI
关键词[WOS]	ONE-CLASS SVM ; RETRIEVAL ; RECOGNITION ; PREDICTION ; FEEDBACK
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000359563500010
引用统计	被引频次：25[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/181661/25285
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, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Ji, Zhong,Pang, Yanwei,Li, Xuelong. Relevance Preserving Projection and Ranking for Web Image Search Reranking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2015,24(11):4137-4147.
APA	Ji, Zhong,Pang, Yanwei,&Li, Xuelong.(2015).Relevance Preserving Projection and Ranking for Web Image Search Reranking.IEEE TRANSACTIONS ON IMAGE PROCESSING,24(11),4137-4147.
MLA	Ji, Zhong,et al."Relevance Preserving Projection and Ranking for Web Image Search Reranking".IEEE TRANSACTIONS ON IMAGE PROCESSING 24.11(2015):4137-4147.

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