Knowledge Commons of Institute of Automation,CAS
A new manifold distance measure for visual object categorization | |
Fengfu Li; Xiayuan Huang; Hong Qiao; Bo Zhang | |
2016 | |
会议名称 | arXiv |
会议录名称 | arXiv |
会议日期 | none |
会议地点 | none |
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. |
关键词 | None |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12834 |
专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 复杂系统管理与控制国家重点实验室 |
通讯作者 | Fengfu Li |
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],2016. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
A New Manifold Dista(226KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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。
修改评论