Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2008.4587800
Title: Discriminative local binary patterns for human detection in personal album
Authors: Mu, Y.
Yan, S. 
Liu, Y.
Huang, T.
Zhou, B.
Issue Date: 2008
Source: Mu, Y.,Yan, S.,Liu, Y.,Huang, T.,Zhou, B. (2008). Discriminative local binary patterns for human detection in personal album. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR : -. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2008.4587800
Abstract: In recent years, local pattern based object detection and recognition have attracted increasing interest in computer vision research community. However, to our best knowledge no previous work has focused on utilizing local patterns for the task of human detection. In this paper we develop a novel human detection system in personal albums based on LBP (local binary pattern) descriptor. Firstly we review the existing gradient based local features widely used in human detection, analyze their limitations and argue that LBP is more discriminative. Secondly, original LBP descriptor does not suit the human detecting problem well due to its high complexity and lack of semantic consistency, thus we propose two variants of LBP: Semantic-LBP and Fourier-LBP. Carefully designed experiments demonstrate the superiority of LBP over other traditional features for human detection. Especially we adopt a random ensemble algorithm for better comparison between different descriptors. All experiments are conducted on INRIA human database. ©2008 IEEE.
Source Title: 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
URI: http://scholarbank.nus.edu.sg/handle/10635/69979
ISBN: 9781424422432
DOI: 10.1109/CVPR.2008.4587800
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