Knowledge Commons of Institute of Automation,CAS
Enhanced Hierarchical Model of Object Recognition Based on Saliency Map and Keypoint | |
Lu Yanfeng(吕彦锋); Kang Taekoo; Zhang Huazhen; Pae Dongsung; Lim Myotaeg | |
2015 | |
会议名称 | 30th Korean Conference of Institute of Control, Robotics and Systems |
会议日期 | 2015.4.22-4.25 |
会议地点 | Seoul, South Korea |
摘要 |
Hierarchical Model and X (HMAX) presents an invariant feature representation, following the mechanisms
of the visual cortex. Although HMAX in object recognition is robust, scale and shift invariant, it has been shown to be sensitive to rotational deformation. To address this, we propose a novel patch selection method saliency and keypoint based patch selection (SKPS). In addition, we suggest an SKPS based HMAX model (S-HMAX). In contrast to HMAX that employs the random patch deriving a significant amount of redundant information, S-HMAX uses SKPS to extract fewer numbers of features with better distinctiveness. To show the effectiveness of S-HMAX, we apply it to object categorization on TU Darmstadt (TUD) database. Experimental results demonstrate that the performance of S-HMAX is a significant improvement on that of conventional HMAX. |
关键词 | Object Recognition Classification Hmax Saliency Map Keypoint |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/15334 |
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 |
推荐引用方式 GB/T 7714 | Lu Yanfeng,Kang Taekoo,Zhang Huazhen,et al. Enhanced Hierarchical Model of Object Recognition Based on Saliency Map and Keypoint[C],2015. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Lu et al. - 2015 - E(324KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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