CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Ordinal preserving projection: a novel dimensionality reduction method for image ranking
Changsheng Li; Jing Liu; Yan Liu; Changsheng Xu; Qingshan Liu; Hanqing Lu
Conference NameInternational Conference on Multimedia Retrieval
Source PublicationProceedings of the 2nd ACM International Conference on Multimedia Retrieval
Conference DateJune 5-8, 2012
Conference PlaceHong Kong, China
AbstractLearning 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.
KeywordDimensionality Reduction Image Ranking Learning To Rank Ordinal Preserving Projection
Document Type会议论文
Corresponding AuthorJing Liu
Recommended Citation
GB/T 7714
Changsheng Li,Jing Liu,Yan Liu,et al. Ordinal preserving projection: a novel dimensionality reduction method for image ranking[C],2012.
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