A Comparative Study of SIFT and its Variants

Measurement Science Review

Cui Zhiming¹  Sheng Victor S.¹  Gong Shengrong²  Wu Jian²  Su Dongliang²  Zhao Pengpeng² 

[1] Department of Computer Science, University of Central Arkansas, Conway 72035, USA;The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China;

关键词: image matching;    local feature;    sift;    pca-sift;    gsift;    csift;    surf;    asift;   

DOI  :  10.2478/msr-2013-0021

来源: DOAJ

【 摘 要 】

SIFT is an image local feature description algorithm based on scale-space. Due to its strong matching ability, SIFT has many applications in different fields, such as image retrieval, image stitching, and machine vision. After SIFT was proposed, researchers have never stopped tuning it. The improved algorithms that have drawn a lot of attention are PCA-SIFT, GSIFT, CSIFT, SURF and ASIFT. In this paper, we first systematically analyze SIFT and its variants. Then, we evaluate their performance in different situations: scale change, rotation change, blur change, illumination change, and affine change. The experimental results show that each has its own advantages. SIFT and CSIFT perform the best under scale and rotation change. CSIFT improves SIFT under blur change and affine change, but not illumination change. GSIFT performs the best under blur change and illumination change. ASIFT performs the best under affine change. PCA-SIFT is always the second in different situations. SURF performs the worst in different situations, but runs the fastest.

【 授权许可】

Unknown