基于随机投影的抗噪声局部二值模式

Shasha Li, Yukai Tu, Weihong Deng, Jiwen Lu
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引用次数: 0

摘要

局部二值模式(LBP)对噪声敏感。LBP算法通过像素与其邻域的相减运算,将局部patch投影到8维向量上。相邻的两个像素值通常非常接近,因此很小的噪声可以改变它们的相对大小,从而导致编码错误。采用另一种投影方法,随机投影,作为替代,我们提出了基于随机投影的局部二值模式(RPLBP),该方法对噪声具有更强的鲁棒性,同时保持计算简单。在FERET数据库上的实验表明,RPLBP对光照、面部表情和年龄变化具有较强的鲁棒性。值得一提的是,所提出的RPBLP的抗噪性能优于LBP和NRLBP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Noise-resistant local binary pattern based on random projection
Local binary pattern (LBP) is sensitive to noise. LBP projects local patch to eight-dimension vector by operating subtractions between pixel and its neighborhood. Two adjacent pixel values are generally very close, thus little noise can change their relative magnitude, leading to coding err. Using another projecting approach, random projection, as an alternate, we propose local binary pattern based on random projection (RPLBP), which is much more robust to noise while remaining computationally simple. Experiments on the aligned FERET database show that RPLBP has outstanding robustness to variations in illumination, facing expressions and aging. It's worth mentioning that the proposed RPBLP demonstrates superior noise-resistant performance to LBP and NRLBP.
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