Similarity Measure Optimization of the SIFT Algorithm

LIN Ke-zheng; WANG Hao; XIN Chen

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Similarity Measure Optimization of the SIFT Algorithm

更新时间：2026-07-10

- Similarity Measure Optimization of the SIFT Algorithm
- Vol. 17, Issue 3, Pages: 78-82(2012)
- 作者机构：
  
  哈尔滨理工大学计算机科学与技术学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.41
- Published：2012
- 稿件说明：
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LIN Ke-zheng, WANG Hao, XIN Chen. Similarity Measure Optimization of the SIFT Algorithm[J]. 2012, 17(3): 78-82.
DOI：

LIN Ke-zheng, WANG Hao, XIN Chen. Similarity Measure Optimization of the SIFT Algorithm[J]. 2012, 17(3): 78-82. DOI：

摘要

针对人脸姿势变化对人脸识别的影响

采用具有良好的尺度、旋转、光照等不变特性的SIFT算法.在分析SIFT特征向量生成过程的基础上

提出一种降低SIFT匹配过程中相似性度量计算代价的方法以提高SIFT特征匹配效率.该方法以绝对值距离和棋盘距离的线性组合距离代替欧式距离作为特征描述子之间的相似性度量.实验结果表明:该方法在保持鲁棒性的同时

可以降低时间复杂度

提高图像匹配的效率.

Abstract

According to the affection of the change of the face position on the face recognition

we adopt the algorithm of Scale Invariant Feature Transformation that has the good invariance of scale

rotation

and illumination. To improve SIFT feature matching algorithm efficiency

a method of reducing similar measure matching cost is presented. The linear combination of cityblock distance and chessboard distance is employed in place of Euclidean distance

and it achieves comparability measurement of feature ...

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Uncorrelated Multiple Information Projection Based on Locality Preserving Projections

Research on the Improved Kernel Discriminant Analysis Based on Face Recognition Algorithm

Small Face Recognition Algorithm Based on Super-resolution Reconstruction

Based on Gabor Characteristics Global Weighted Sparse Representation

Scale-invariant Feature Transform Based on Clustering in Face Recognition

Related Author

WU Qing-ying

CHEN Dong-mei

LIN Ke-zheng

ZHAO Ying

LIN Ke-zheng

LI Zi-xiang

CHENG Wei-yue

LI Jing-yu

Related Institution

哈尔滨理工大学计算机科学与技术学院

伊春职业学院机电工程系

Heilongjiang Administrative Cadre Institute of Politics and Law

1 School of Computer Science and Technology, Harbin University of Science and Technology

2Heilongjiang College of Business and Technology

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