Ordinal preserving projection: a novel dimensionality reduction method for image ranking

	Ordinal preserving projection: a novel dimensionality reduction method for image ranking
	Changsheng Li; Jing Liu; Yan Liu; Changsheng Xu; Qingshan Liu; Hanqing Lu
	2012
会议名称	International Conference on Multimedia Retrieval
会议录名称	Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
会议日期	June 5-8, 2012
会议地点	Hong Kong, China
摘要	Learning to rank has been demonstrated as a powerful tool for image ranking, but the issue of the "curse of dimensionality" is a key challenge of learning a ranking model from a large image database. This paper proposes a novel dimensionality reduction algorithm named ordinal preserving projection (OPP) for learning to rank. We first define two matrices, which work in the row direction and column direction respectively. The two matrices aim at leveraging the global structure of the data set and ordinal information of the observations. By maximizing the corresponding objective functions, we can obtain two optimal projection matrices mapping original data points into low-dimensional subspace, in which both global structure and ordinal information can be preserved. The experiments are conducted on the public available MSRA-MM image data set and "Web Queries" image data set, and the experimental results demonstrate the effectiveness of the proposed method.
关键词	Dimensionality Reduction Image Ranking Learning To Rank Ordinal Preserving Projection
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13449
专题	紫东太初大模型研究中心_图像与视频分析
通讯作者	Jing Liu
推荐引用方式 GB/T 7714	Changsheng Li,Jing Liu,Yan Liu,et al. Ordinal preserving projection: a novel dimensionality reduction method for image ranking[C],2012.