Compact descriptor for local feature using dominating centre‐symmetric local binary pattern
Yingying Li, Jieqing Tan, Jinqin Zhong, Qiang Chen · IET Computer Vision · 2015
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.