Heterogeneous Metric Learning for Cross-Modal Multimedia Retrieval
文献类型：会议论文
| 作者 | Deng, Jun ; Du, Liang ; Shen, Yi-Dong |
| 出版日期 | 2013 |
| 会议名称 | 14th International Conference on Web Information Systems Engineering (WISE) |
| 会议日期 | OCT 13-15, 2013 |
| 会议地点 | Nanjing, PEOPLES R CHINA |
| 关键词 | Metric Learning Heterogeneous Spaces Multimedia |
| 页码 | 43-56 |
| 中文摘要 | Due to the massive explosion of multimedia content on the web, users demand a new type of information retrieval, called cross-modal multimedia retrieval where users submit queries of one media type and get results of various other media types. Performing effective retrieval of heterogeneous multimedia content brings new challenges. One essential aspect of these challenges is to learn a heterogeneous metric between different types of multimedia objects. In this paper, we propose a Bayesian personalized ranking based heterogeneous metric learning (BPRHML) algorithm, which optimizes for correctly ranking the retrieval results. It uses pairwise preference constraints as training data and explicitly optimizes for preserving these constraints. To further encouraging the smoothness of learning results, we integrate graph regularization with Bayesian personalized ranking. The experimental results on two publicly available datasets show the effectiveness of our method. |
| 英文摘要 | Due to the massive explosion of multimedia content on the web, users demand a new type of information retrieval, called cross-modal multimedia retrieval where users submit queries of one media type and get results of various other media types. Performing effective retrieval of heterogeneous multimedia content brings new challenges. One essential aspect of these challenges is to learn a heterogeneous metric between different types of multimedia objects. In this paper, we propose a Bayesian personalized ranking based heterogeneous metric learning (BPRHML) algorithm, which optimizes for correctly ranking the retrieval results. It uses pairwise preference constraints as training data and explicitly optimizes for preserving these constraints. To further encouraging the smoothness of learning results, we integrate graph regularization with Bayesian personalized ranking. The experimental results on two publicly available datasets show the effectiveness of our method. |
| 收录类别 | CPCI |
| 会议录出版地 | SPRINGER-VERLAG BERLIN |
| 语种 | 英语 |
| ISSN号 | 0302-9743 |
| ISBN号 | 978-3-642-41230-1; 978-3-642-41229-5 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16527]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 推荐引用方式 GB/T 7714 | Deng, Jun,Du, Liang,Shen, Yi-Dong. Heterogeneous Metric Learning for Cross-Modal Multimedia Retrieval[C]. 见:14th International Conference on Web Information Systems Engineering (WISE). Nanjing, PEOPLES R CHINA. OCT 13-15, 2013. |
入库方式： OAI收割
来源：软件研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。