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Robust image region descriptor using local derivative ordinal binary pattern

[+] Author Affiliations
Jun Shang

Huazhong University of Science and Technology, School of Computer Science and Technology, Luoyu Road No. 1037, Wuhan 430074, China

Hubei University of Education, Hubei Co-Innovation Center of Basic Education Information Technology Services, High-tech Road No. 129, Wuhan 430205, China

Chuanbo Chen, Xiaobing Pei, He Tang, Mudar Sarem

Huazhong University of Science and Technology, School of Software Engineering, Luoyu Road No. 1037, Wuhan 430074, China

Hu Liang

Huazhong University of Science and Technology, School of Computer Science and Technology, Luoyu Road No. 1037, Wuhan 430074, China

J. Electron. Imaging. 24(3), 033009 (May 20, 2015). doi:10.1117/1.JEI.24.3.033009
History: Received November 27, 2014; Accepted April 8, 2015
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Abstract.  Binary image descriptors have received a lot of attention in recent years, since they provide numerous advantages, such as low memory footprint and efficient matching strategy. However, they utilize intermediate representations and are generally less discriminative than floating-point descriptors. We propose an image region descriptor, namely local derivative ordinal binary pattern, for object recognition and image categorization. In order to preserve more local contrast and edge information, we quantize the intensity differences between the central pixels and their neighbors of the detected local affine covariant regions in an adaptive way. These differences are then sorted and mapped into binary codes and histogrammed with a weight of the sum of the absolute value of the differences. Furthermore, the gray level of the central pixel is quantized to further improve the discriminative ability. Finally, we combine them to form a joint histogram to represent the features of the image. We observe that our descriptor preserves more local brightness and edge information than traditional binary descriptors. Also, our descriptor is robust to rotation, illumination variations, and other geometric transformations. We conduct extensive experiments on the standard ETHZ and Kentucky datasets for object recognition and PASCAL for image classification. The experimental results show that our descriptor outperforms existing state-of-the-art methods.

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Citation

Jun Shang ; Chuanbo Chen ; Xiaobing Pei ; Hu Liang ; He Tang, et al.
"Robust image region descriptor using local derivative ordinal binary pattern", J. Electron. Imaging. 24(3), 033009 (May 20, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.3.033009


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