Sparse semantic metric learning for image retrieval
文献类型：期刊论文
| 作者 | Liu, Jing ; Li, Zechao; Lu, Hanqing
|
| 刊名 | MULTIMEDIA SYSTEMS
 |
| 出版日期 | 2014-11-01 |
| 卷号 | 20期号:6页码:635-643 |
| 关键词 | Sparse metric Semantic distance metric Social image Image retrieval |
| 英文摘要 | Typical content-based image retrieval solutions usually cannot achieve satisfactory performance due to the semantic gap challenge. With the popularity of social media applications, large amounts of social images associated with user tagging information are available, which can be leveraged to boost image retrieval. In this paper, we propose a sparse semantic metric learning (SSML) algorithm by discovering knowledge from these social media resources, and apply the learned metric to search relevant images for users. Different from the traditional metric learning approaches that use similar or dissimilar constraints over a homogeneous visual space, the proposed method exploits heterogeneous information from two views of images and formulates the learning problem with the following principles. The semantic structure in the text space is expected to be preserved for the transformed space. To prevent overfitting the noisy, incomplete, or subjective tagging information of images, we expect that the mapping space by the learned metric does not deviate from the original visual space. In addition, the metric is straightforward constrained to be row-wise sparse with the l(2,1)-norm to suppress certain noisy or redundant visual feature dimensions. We present an iterative algorithm with proved convergence to solve the optimization problem. With the learned metric for image retrieval, we conduct extensive experiments on a real-world dataset and validate the effectiveness of our approach compared with other related work. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Information Systems ; Computer Science, Theory & Methods |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | NONLINEAR DIMENSIONALITY REDUCTION ; CLASSIFICATION ; CONSTRAINTS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000344065400002 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3360]  |
| 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 |
| 作者单位 | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Jing,Li, Zechao,Lu, Hanqing. Sparse semantic metric learning for image retrieval[J]. MULTIMEDIA SYSTEMS,2014,20(6):635-643. |
| APA | Liu, Jing,Li, Zechao,&Lu, Hanqing.(2014).Sparse semantic metric learning for image retrieval.MULTIMEDIA SYSTEMS,20(6),635-643. |
| MLA | Liu, Jing,et al."Sparse semantic metric learning for image retrieval".MULTIMEDIA SYSTEMS 20.6(2014):635-643. |
入库方式： OAI收割
来源：自动化研究所
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