COLOR-SURF: A SURF DESCRIPTOR WITH LOCAL KERNEL COLOR HISTOGRAMS

被引：25

作者：

Fan, Peng ^{[1
]}

Men, Aidong ^{[1
]}

Chen, Mengyang ^{[1
]}

Yang, Bo ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Multimedia Technol Ctr, Sch Informat & Commun Engn, Beijing 100088, Peoples R China

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON NETWORK INFRASTRUCTURE AND DIGITAL CONTENT, PROCEEDINGS | 2009年

关键词：

SURF; image matching; local kernel color histogram; Bhattacharyya distance;

D O I：

10.1109/ICNIDC.2009.5360809

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

SIFT (Scale Invariant Feature Transform) is an important local invariant feature descriptor. Since its expensive computation, SURF (Speeded-Up Robust Features) is proposed. Both of them are designed mainly for gray images. However, color provides valuable information in object description and matching tasks. To overcome the drawback and to increase the descriptor's distinctiveness, this paper presents a novel feature descriptor which combines local kernel color histograms and Haar wavelet responses to construct the feature vector. So the descriptor is a two elements vector. In image matching process, SURF descriptor is first compared, then the unmatched points are computed by Bhattacharyya distance between their local kernel color histograms. Extensive experimental evaluations show that the method has better robustness than the original SURF. The ratio of correct matches is increased by about 8.9% in the given dataset.

引用

页码：726 / 730

页数：5

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