Journal of shanghai Jiaotong University (Science) ›› 2012, Vol. 17 ›› Issue (2): 147-152.doi: 10.1007/s12204-012-1244-6

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Improved Global Context Descriptor for Describing Interest Regions

Improved Global Context Descriptor for Describing Interest Regions

LIU Jing-neng (刘景能), ZENG Gui-hua (曾贵华)   

  1. (State Key Laboratory of Advanced Optical Communication Systems and Networks, Key Laborotory on Navigation and Location-Based Service, Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200240, China)
  2. (State Key Laboratory of Advanced Optical Communication Systems and Networks, Key Laborotory on Navigation and Location-Based Service, Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200240, China)
  • Online:2012-04-28 Published:2012-05-31
  • Contact: LIU Jing-neng (刘景能), E-mail: bzljn@163.com

Abstract: The global context (GC) descriptor is improved for describing interest regions, uses gradient orientation for binning, and thus provides more robust invariance for geometric and photometric transformations. The performance of the improved GC (IGC) to image matching is studied through extensive experiments on the Oxford Affine dataset. Empirical results indicate that the proposed IGC yields quite stable and robust results, significantly outperforms the original GC, and also can outperform the classical scale-invariant feature transform (SIFT) in most of the test cases. By integrating the IGC to the SIFT, the resulting of hybrid SIFT+IGC performs best over all other single descriptors in these experimental evaluations with various geometric transformations.

Key words:

global context (GC)| scale-invariant feature transform (SIFT)| region description| image matching

摘要: The global context (GC) descriptor is improved for describing interest regions, uses gradient orientation for binning, and thus provides more robust invariance for geometric and photometric transformations. The performance of the improved GC (IGC) to image matching is studied through extensive experiments on the Oxford Affine dataset. Empirical results indicate that the proposed IGC yields quite stable and robust results, significantly outperforms the original GC, and also can outperform the classical scale-invariant feature transform (SIFT) in most of the test cases. By integrating the IGC to the SIFT, the resulting of hybrid SIFT+IGC performs best over all other single descriptors in these experimental evaluations with various geometric transformations.

关键词:

global context (GC)| scale-invariant feature transform (SIFT)| region description| image matching

CLC Number: