Improved Global Context Descriptor for Describing Interest Regions

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 A?ne dataset.Empirical results indicate that the proposed IGC yields quite stable and robust results,signi?cantly 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.

The global context （GC） descriptor is improved for describing interest regions, uses gradient orien- tation 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 descriDtors in these experimental evaluations with various geometric transformations.