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Journal of Electronic Imaging

Haar-like compact local binary pattern for illumination-robust feature matching
Author(s): Bongjoe Kim; Jihoon Choi; Sanghyun Joo; Kwanghoon Sohn
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Paper Abstract

A novel method for local image descriptor called a Haar-like compact local binary pattern (HC-LBP) is presented, which is robust to Gaussian noise and illumination changes and reduces the dimension of features by modifying the local binary pattern (LBP) method in two ways. First, the binary Haar-like feature method is applied to calculate region-based rank order. The binary Haar-like feature method maintains only the ordinal relationship and not the difference of intensity. Using a region-based binary operation, the proposed local image descriptor becomes more robust to Gaussian noise and illumination changes. Second, a HC-LBP feature is generated by applying the binary Haar-like feature according to the edge class in order to reduce feature dimensions and increase the discriminating power of each feature. In the feature matching experiment, the proposed method outperforms the popular scale invariant feature transform, center-symmetric local binary pattern and center-symmetric local ternary pattern in the presence of illumination changes, Gaussian noise, image blurring, and viewpoint change. Compared with popular local feature descriptors, the proposed method could get more than 0.1 increases in recall. Also, the HC-LBP descriptor could be used for many computer vision applications such as image retrieval, face recognition, and texture recognition.

Paper Details

Date Published: 22 November 2012
PDF: 9 pages
J. Electron. Imaging. 21(4) 043014 doi: 10.1117/1.JEI.21.4.043014
Published in: Journal of Electronic Imaging Volume 21, Issue 4
Show Author Affiliations
Bongjoe Kim, Yonsei Univ. (Korea, Republic of)
Jihoon Choi, Yonsei Univ. (Korea, Republic of)
Sanghyun Joo, Yonsei Univ. (Korea, Republic of)
Kwanghoon Sohn, Yonsei Univ. (Korea, Republic of)


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