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9 August 2018 An optimized SIFT algorithm based on color space normalization
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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%.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tuochi Jiang, Desheng Wen, Zongxi Song, Wei Gao, Chao Shen, and 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); https://doi.org/10.1117/12.2503039
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