Enhanced Hierarchical Model of Object Recognition Based on Saliency Map and Keypoint | |
Lu Yanfeng(吕彦锋)![]() | |
2015 | |
会议日期 | 2015.4.22-4.25 |
会议地点 | Seoul, South Korea |
关键词 | Object Recognition Classification Hmax Saliency Map Keypoint |
英文摘要 |
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. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/15334] ![]() |
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 |
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]. 见:. Seoul, South Korea. 2015.4.22-4.25. |
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