An Improved SIFT Algorithm for Image Matching

DOI

10.2991/emcs-16.2016.271How to use a DOI?

Keywords

SIFT algorithm; Image matching; Keypoints; Quasi Euclidean; Scale space

Abstract

Aiming at the problems of large calculating scale and high complexity in Scale Invariant Feature Transform (SIFT) feature matching algorithm, this paper presents an improved SIFT feature matching algorithm based on quasi Euclidean distance. The traditional Euclidean distance can only calculate out the variance of the two images to the corresponding pixel, so when a slight shift or distortion occurs in the image, it may produce a large deviation. The quasi Euclidean distance instead of Euclidean distance is as the similarity measure of feature descriptors to improve the SIFT feature matching. It reduces the dimensions of SIFT feature vector to improve the efficiency of feature matching. Experimental results show that under the condition of keeping the image matching rate and algorithm robust, the method can not only improve the matching accuracy but also shorten the matching time. The new algorithm has better performance than traditional algorithm, it is possible and valid, which are useful for the fields of image recognition, image reconstruction, etc.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hui Zhang
AU  - Dan Ren
AU  - Fengzhong Zhang
AU  - Li Wang
AU  - Xin Wang
AU  - Hongliang Kan
AU  - Jiuyi Lü
AU  - Bin Wang
PY  - 2016/01
DA  - 2016/01
TI  - An Improved SIFT Algorithm for Image Matching
BT  - Proceedings of the 2016 International Conference on Education, Management, Computer and Society
PB  - Atlantis Press
SP  - 1103
EP  - 1106
SN  - 2352-538X
UR  - https://doi.org/10.2991/emcs-16.2016.271
DO  - 10.2991/emcs-16.2016.271
ID  - Zhang2016/01
ER  -