An optimized SIFT algorithm based on color space normalization

Tuochi Jiang; Desheng Wen; Zongxi Song; Wei Gao; Chao Shen; Feng Wang

doi:10.1117/12.2503039

9 August 2018 An optimized SIFT algorithm based on color space normalization

Tuochi Jiang, Desheng Wen, Zongxi Song, Wei Gao, Chao Shen, Feng Wang

Author Affiliations +

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 1080608 (2018) https://doi.org/10.1117/12.2503039
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

The Scale Invariant Feature Transform (SIFT) algorithm has been widely used for its excellent stability in rotation, scale and affine transformation. The local SIFT descriptor has excellent accuracy and robustness. However, it is only based on gray scale ignoring the overall color information of the image resulting in poorly recognizing to the images with rich color details. We proposed an optimized method of SIFT algorithm in this paper which shows superior performance in feature extraction and matching. RGB color space normalization is used to eliminate the effects of illumination position and intensity invariant on the image. Then we proposed a novel similarity retrieval method, which used K nearest neighbor search strategy by constructing K-D tree (k-dimensional tree), to process the key points extracted from the normalized color space. The key points of RGB space are filtered and combined efficiently. Experimental results demonstrate that the performance of the optimized algorithm is obviously better than the original SIFT algorithm in matching. The average matching accuracy of test samples is 87.05%, an average increase of 18.21%.

Citation Download Citation

Tuochi Jiang, Tuochi Jiang, Desheng Wen, Desheng Wen, Zongxi Song, Zongxi Song, Wei Gao, Wei Gao, Chao Shen, Chao Shen, Feng Wang, Feng Wang, } "An optimized SIFT algorithm based on color space normalization", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080608 (9 August 2018); doi: 10.1117/12.2503039; https://doi.org/10.1117/12.2503039

ACCESS THE FULL ARTICLE

PERSONAL SIGN IN
Full access may be available with your subscription

Email or Username Forgot your username?

Password Forgot your password?

Show

Remember Email/Username on this computer

Remember Password

No SPIE account? Create an account
or Institutional Sign In via Shibboleth

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $14.40

Non-members: $18.00 ADD TO CART

Show All Keywords

Keywords/Phrases

Search In:

Publication Years