Ordinal preserving projection: a novel dimensionality reduction method for image ranking
文献类型：会议论文
| 作者 | Changsheng Li; Jing Liu ; Yan Liu; Changsheng Xu; Qingshan Liu; Hanqing Lu
|
| 出版日期 | 2012 |
| 会议日期 | June 5-8, 2012 |
| 会议地点 | Hong Kong, China |
| 关键词 | Dimensionality Reduction Image Ranking Learning To Rank Ordinal Preserving Projection |
| 英文摘要 | 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. |
| 会议录 | Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13449]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 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]. 见:. Hong Kong, China. June 5-8, 2012. |
入库方式： OAI收割
来源：自动化研究所
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