Open AccessCompact descriptor for local feature using dominating centre‐symmetric local binary pattern
Author(s) -
Li Yingying,
Tan Jieqing,
Zhong Jinqin,
Chen Qiang
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0394
Subject(s) - local binary patterns , pattern recognition (psychology) , artificial intelligence , scale invariant feature transform , histogram , mathematics , robustness (evolution) , invariant (physics) , binary number , feature extraction , compact space , computer vision , feature (linguistics) , computer science , image (mathematics) , mathematical physics , biochemistry , chemistry , linguistics , philosophy , arithmetic , pure mathematics , gene
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.