CGCI-SIFT: A more efficient and compact representation of local descriptor

Dongliang Su, Jian Wu, Zhiming Cui, Victor S. Sheng, Shengrong Gong

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish
Pages (from-to)132-141
Number of pages10
JournalMeasurement Science Review
Issue number3
StatePublished - Jun 2013


  • Descriptor
  • Image matching
  • Real-time
  • SIFT


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