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Object detection using Non-Redundant Local Binary Patterns

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4 Author(s)
Duc Thanh Nguyen ; Advanced Multimedia Research Lab, ICT Research Institute, School of Computer Science and Software Engineering, University of Wollongong, Australia ; Zhimin Zong ; Philip Ogunbona ; Wanqing Li

Local Binary Pattern (LBP) as a descriptor, has been successfully used in various object recognition tasks because of its discriminative property and computational simplicity. In this paper a variant of the LBP referred to as Non-Redundant Local Binary Pattern (NRLBP) is introduced and its application for object detection is demonstrated. Compared with the original LBP descriptor, the NRLBP has advantage of providing a more compact description of object's appearance. Furthermore, the NRLBP is more discriminative since it reflects the relative contrast between the background and foreground. The proposed descriptor is employed to encode human's appearance in a human detection task. Experimental results show that the NRLBP is robust and adaptive with changes of the background and foreground and also outperforms the original LBP in detection task.

Published in:

2010 IEEE International Conference on Image Processing

Date of Conference:

26-29 Sept. 2010