Multiple similarities based kernel subspace learning for image classification
文献类型：期刊论文
| 作者 | Yan, W; Liu, QS; Lu, HQ ; Ma, SD; Narayanan, PJ; Nayar, SK; Shum, HY
|
| 刊名 | COMPUTER VISION - ACCV 2006, PT II
 |
| 出版日期 | 2006 |
| 卷号 | 3852页码:244-253 |
| 英文摘要 | In this paper, we propose a new method for image classification, in which matrix based kernel features are designed to capture the multiple similarities between images in different low-level visual cues. Based on the property that dot product kernel can be regarded as a similarity measure, we apply kernel functions to different low-level visual features respectively to measure the similarities between two images, and obtain a kernel feature matrix for each image. In order to deal with the problems of over fitting and numerical computation, a revised version of Two-Dimensional PCA algorithm is developed to learn intrinsic subspace of matrix features for classification. Extensive experiments on the Corel database show the advantage of the proposed method. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | HUMAN FACES |
| 收录类别 | ISTP ; SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000235773200025 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9229]  |
| 专题 | 自动化研究所_09年以前成果 |
| 作者单位 | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yan, W,Liu, QS,Lu, HQ,et al. Multiple similarities based kernel subspace learning for image classification[J]. COMPUTER VISION - ACCV 2006, PT II,2006,3852:244-253. |
| APA | Yan, W.,Liu, QS.,Lu, HQ.,Ma, SD.,Narayanan, PJ.,...&Shum, HY.(2006).Multiple similarities based kernel subspace learning for image classification.COMPUTER VISION - ACCV 2006, PT II,3852,244-253. |
| MLA | Yan, W,et al."Multiple similarities based kernel subspace learning for image classification".COMPUTER VISION - ACCV 2006, PT II 3852(2006):244-253. |
入库方式： OAI收割
来源：自动化研究所
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