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
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
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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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