Compact descriptor for local feature using dominating centre-symmetric local binary pattern

Yingying Li; Jieqing Tan; Jinqin Zhong; Qiang Chen

Compact descriptor for local feature using dominating centre-symmetric local binary pattern

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Author(s): Yingying Li ^{1, 2} ; Jieqing Tan ² ; Jinqin Zhong ² ; Qiang Chen ²
- Affiliations: 1: School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230601, People's Republic of China ;
  2: School of Computer and Information, Hefei University of Technology, Hefei 230009, People's Republic of China
Source: Volume 10, Issue 1, February 2016, p. 36 – 42
DOI: 10.1049/iet-cvi.2014.0394 , Print ISSN 1751-9632, Online ISSN 1751-9640

Received 10/11/2014, Accepted 14/06/2015, Revised 12/03/2015, Published 01/02/2016

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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Compact descriptor for local feature using dominating centre-symmetric local binary pattern

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