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
Conference Name30th Korean Conference of Institute of Control, Robotics and Systems
Conference Date2015.4.22-4.25
Conference PlaceSeoul, South Korea
Abstract
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.
KeywordObject Recognition Classification Hmax Saliency Map Keypoint
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15334
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Recommended Citation
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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