CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor

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This paper proposes a novel invariant local descriptor, a combination of gradient histograms with contrast intensity (CGCI), for image matching and object recognition. Considering the different contributions of sub-regions inside a local interest region to an interest point, we divide the local interest region around the interest point into two main sub-regions: an inner region and a peripheral region. Then we describe the divided regions with gradient histogram information for the inner region and contrast intensity information for the peripheral region respectively. The contrast intensity information is defined as intensity difference between an interest point and other pixels in the local region. Our experimental results demonstrate that the proposed descriptor performs better than SIFT and its variants PCA-SIFT and SURF with various optical and geometric transformations. It also has better matching efficiency than SIFT and its variants PCA-SIFT and SURF, and has the potential to be used in a variety of realtime applications.

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Measurement Science Review

The Journal of Institute of Measurement Science of Slovak Academy of Sciences

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IMPACT FACTOR 2017: 1.345
5-year IMPACT FACTOR: 1.253



CiteScore 2017: 1.61

SCImago Journal Rank (SJR) 2017: 0.441
Source Normalized Impact per Paper (SNIP) 2017: 0.936

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