Robust method for interest region description based on local intensity binary pattern

Yi Yang; Fajie Duan; Jiajia Jiang; Ling Ma; Hao Zheng

doi:10.1117/1.JEI.26.4.043025

28 August 2017 Robust method for interest region description based on local intensity binary pattern

Yi Yang, Fajie Duan, Jiajia Jiang, Ling Ma, Hao Zheng

Author Affiliations +

Funded by: National High Technology Research and Development Program of China, National “863 Plan” Project, National High-Tech Research and Development Program (“863”) of China, Natural Science Foundation of China, Key Laboratory of Micro-Opto-Electro Mechanical System Technology, Tianjin University, Ministry of Education, Photoelectric Information and Instrument—Engineering Research Center of Beijing Open Project, National Marine Economy Innovation Development Area Demonstration Project, Program for Changjiang Scholars and Innovative Research Team in University, Doctoral Fund of Ministry of Education of China, Doctoral Scientific Fund Project of the Ministry of Education of China, Aero-Science, Aviation Science Foundation of China

Journal of Electronic Imaging, Vol. 26, Issue 4, 043025 (August 2017). https://doi.org/10.1117/1.JEI.26.4.043025

Abstract

This paper proposes a robust method based on a local intensity binary pattern for interest feature description. To avoid estimating reference orientation, local features were calculated on a rotation invariant system. Different from the local binary patterns (LBP) and center-symmetric-LBP operator, our proposed local circular contrast pattern (LCCP) operator calculates a local binary feature by comparing the relative intensity order information of each two adjacent elements in the sequence consisting of the sampling point and its neighboring points. To evaluate the performance of our proposed descriptor LCCP and other existing descriptors (e.g., scale-invariant feature transform, DAISY, HRI-CSLTP, multisupport region order-based gradient histogram-single, local intensity order pattern), image matching experiments were first conducted on the Oxford dataset, additional image pairs with complex light changes, image sequences with different noise, and three-dimensional objects dataset. To further evaluate the discriminative ability of local descriptors, a simple object recognition experiment was carried out on three public datasets. The experimental results show that our descriptor LCCP exhibits a better performance and robustness than other evaluated descriptors.

Citation Download Citation

Yi Yang, Fajie Duan, Jiajia Jiang, Ling Ma, and Hao Zheng "Robust method for interest region description based on local intensity binary pattern," Journal of Electronic Imaging 26(4), 043025 (28 August 2017). https://doi.org/10.1117/1.JEI.26.4.043025

Received: 28 March 2017; Accepted: 9 August 2017; Published: 28 August 2017

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KEYWORDS

Binary data

Image compression

3D image processing

Object recognition

Sensors

Error analysis

Image analysis

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