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The authors propose a terse texture feature, called the dominant centre-symmetric local binary pattern (DCSLBP), which has similar distinctiveness and half dimension compared against original centre-symmetric local binary pattern (CS-LBP). On the basis of DCSLBP histogram and an improved construction, a compact descriptor for local feature is presented. To assess the proposed descriptor with the state-of-the-art in performance and dimension, the authors extend it to two variants with different dimensions using the existing method. These descriptors are compared with scale-invariant feature transform (SIFT), multisupport region rotation and intensity monotonic invariant descriptor (MRRID), orthagonal combination local binary pattern (OC-LBP) in interest region matching and in the application of object recognition. The experiments demonstrate the proposed descriptor's compactness and robustness to various image transformations, especially to large illumination change.
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