Mining concise and distinctive affine-stable features for object detection in large corpus

doi:10.1080/00207160.2011.583350

	Mining concise and distinctive affine-stable features for object detection in large corpus
	Gao, Ke 1; Zhang, Yongdong 1; Zhang, Wei 1,2; Lin, Shouxun 1
	2011
发表期刊	INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
ISSN	0020-7160
卷号	88 期号:18 页码:3953-3962
摘要	Invariant features extraction is important for object detection. Affine-SIFT (ASIFT) [J.M. Morel and G. Yu, ASIFT: A new framework for fully affine invariant image comparison, SIAM J. Imaging Sci. 2(2) (2009)] has been proved to be fully affine-invariant. However, the high cost of memory and query time hampers its application in large-scale object detection tasks. In this paper, we present a novel algorithm for mining concise and distinctive invariant features called affine-stable characteristics (ASC). Two new notions, global stability and local stability, are introduced to calculate the robustness of each feature from two mutually complementary aspects. Furthermore, to make these stable characteristics more distinctive, spatial information taken from several representative scales is encoded in a concise method. Experiments show that the robustness of our ASC is comparable with ASIFT, while the cost of memory can be reduced significantly to only 5%. Moreover, compared with the traditional SIFT method [ D. Lowe, Distinctive image features from scale invariant keypoints, Int. J. Comput. Vis. 60(2) (2004), pp. 91-110], the accuracy of object detection can be improved 38.6% by our ASC using similar amount of features.
关键词	object detection feature extraction affine-stable features data mining spatial information
DOI	10.1080/00207160.2011.583350
收录类别	SCI
语种	英语
WOS研究方向	Mathematics
WOS类目	Mathematics, Applied
WOS记录号	WOS:000297692200013
出版者	TAYLOR & FRANCIS LTD
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/12794
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Gao, Ke
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Chinese Acad Sci, Grad Univ, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Gao, Ke,Zhang, Yongdong,Zhang, Wei,et al. Mining concise and distinctive affine-stable features for object detection in large corpus[J]. INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS,2011,88(18):3953-3962.
APA	Gao, Ke,Zhang, Yongdong,Zhang, Wei,&Lin, Shouxun.(2011).Mining concise and distinctive affine-stable features for object detection in large corpus.INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS,88(18),3953-3962.
MLA	Gao, Ke,et al."Mining concise and distinctive affine-stable features for object detection in large corpus".INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 88.18(2011):3953-3962.