尺度不变特征变换
人工智能
计算机科学
维数之咒
探测器
点(几何)
兴趣点检测
兴趣点
排名(信息检索)
数学
模式识别(心理学)
特征提取
计算机视觉
图像(数学)
图像处理
边缘检测
电信
几何学
作者
Krystian Mikolajczyk,Cordelia Schmid
出处
期刊:Computer Vision and Pattern Recognition
日期:2003-11-21
被引量:519
标识
DOI:10.1109/cvpr.2003.1211478
摘要
In this paper we compare the performance of interest point descriptors. Many different descriptors have been proposed in the literature. However, it is unclear which descriptors are more appropriate and how their performance depends on the interest point detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the point detector. Our evaluation uses as criterion detection rate with respect to false positive rate and is carried out for different image transformations. We compare SIFT descriptors (Lowe, 1999), steerable filters (Freeman and Adelson, 1991), differential invariants (Koenderink ad van Doorn, 1987), complex filters (Schaffalitzky and Zisserman, 2002), moment invariants (Van Gool et al., 1996) and cross-correlation for different types of interest points. In this evaluation, we observe that the ranking of the descriptors does not depend on the point detector and that SIFT descriptors perform best. Steerable filters come second ; they can be considered a good choice given the low dimensionality.
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