基于余弦核函数的SIFT描述子改进算法  

An Improved SIFT Descriptor Based on Cosine Kernel Function

作　　者：丁理想[1] 何川[1] 李书杰[1] DING Lixiang;HE Chuan;LI Shujie(School of Computer and Information, Hefei University of Technology, Hefei Anhui 230009, China)

机构地区：[1]合肥工业大学计算机与信息学院,安徽合肥230009

出　　处：《图学学报》2017年第3期373-381,共9页Journal of Graphics

基　　金：高等学校博士学科点专项科研基金项目(20120111110003)

摘　　要：原始的SIFT特征描述子维数较高,包含较多的冗余数据,因而在各类应用中需要耗费较多的时间。文中考虑到SIFT描述子内部梯度向量之间的关系,采用基于余弦核函数的核主成分分析法对SIFT特征描述子进行降维操作。首先,提取样本图像的SIFT特征描述子,利用余弦函数生成核主成分矩阵,提取其在主方向上的投影矩阵;然后,利用该投影矩阵对新采集的描述子进行降维操作。实验中采用图像匹配的方式比较描述子性能,实验表明:该算法可以有效降低特征描述子的维数;同时,在不降低匹配准确率的情况下,能够获得比SIFT多的匹配点,而且时间性能显著提高。The SIFT descriptor has been widely used in the field of computer vision thanks to its various invariant attributes;however,its high dimensionality results in redundant data and makes it time-consuming for application.Therefore,a novel algorithm,considering the inner relationship between gradient vectors in SIFT descriptor,is presented in this paper,which utilizes the principal component analysis method based on cosine kernel function.First,a principal component matrix,which is used to compute the principal direction of the projection matrix,is generated by using cosine kernel function to extract SIFT descriptors from the sample images.Then,the projection matrix is applied to the dimensionality reduction of the SIFT descriptors from the new images.In the experiment,we evaluate the performance of descriptors by means of image matching.The results indicate that our method can efficiently reduce the dimensionality and also obtain more matches without sacrificing the matching accuracy and meanwhile improve time performance.

关 键 词：模式识别 图像配准 特征描述子 主成分分析法 余弦核函数 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]