A new manifold distance measure for visual object categorization
文献类型：会议论文
| 作者 | Fengfu Li; Xiayuan Huang ; Hong Qiao; Bo Zhang
|
| 出版日期 | 2016 |
| 会议名称 | arXiv |
| 会议日期 | none |
| 会议地点 | none |
| 关键词 | none |
| 通讯作者 | Fengfu Li |
| 英文摘要 | Manifold distances are very effective tools for visual object recognition. However, most of the traditionalmanifold distances between images are based on the pixel-level comparison and thus easily affected by image rotations and translations. In this paper, we propose a new manifold distance to model the dissimilarities between visual objects based on the Complex Wavelet Structural Similarity (CW-SSIM) index. The proposed distance is more robust to rotations and translations of images than the traditionalmanifold distance and the CW-SSIM index based distance. In addition, the proposed distance is combined with the k-medoids clustering method to derive a new clustering method for visual objectcategorization. Experiments on Coil-20, Coil-100 and Olivetti Face Databases show that the proposeddistance measure is better for visual object categorization than both the traditional manifold distances and the CW-SSIM index based distances. |
| 会议录 | arXiv
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12834]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 自动化研究所_复杂系统管理与控制国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Fengfu Li,Xiayuan Huang,Hong Qiao,et al. A new manifold distance measure for visual object categorization[C]. 见:arXiv. none. none. |
入库方式： OAI收割
来源：自动化研究所
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