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基于改进WLD的纹理特征提取方法 预览 被引量:2

Texture Feature Extraction Method Based on Improved WLD
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摘要 在纹理分类应用背景下,原始韦伯局部描述符(WLD)对纹理模式区分能力有限。针对该问题,提出一种基于正负梯度改进的WLD(WLD-PNG)。利用局部窗内像素点间灰度变化的正负梯度构建纹理特征描述符,通过分离计算正负梯度的差分激励算子,保留灰度等级变化的正负性信息,以增强纹理模式的可区分性,运用均匀局部二值模式(u LBP)提取灰度等级变化的空间分布结构信息,并提高纹理模式的识别能力,使用均匀量化和编码技术将差分激励算子与u LBP结合,从而描述图像的纹理特征。在Brodatz和KTH-TIPS2-a纹理库上进行对比实验,结果表明,与原始WLD,u LBP,WLD+u LBP及已有改进的WLD等方法相比,WLD-PNG在提高纹理分类性能的同时,具有较好的稳健性和较低的计算复杂度。 Aiming at the shortage of the discriminative ability to texture patterns for image texture classification using Weber Local Descriptor( WLD),an improved WLD based on Positive and Negative Gradient Features( WLD-PNG) is proposed. Positive and negative gradients are characterized by computing positive and negative differential excitations for preserving signed grayscale change information,and local texture structure information is represented by uniform Local Binary Patterns( u LBP). Combine both of them to build the image texture feature. The comparing experiments on the Brodatz and KTH-TIPS2-a texture databases demonstrate that WLD-PNG improves the distinctiveness of texture patterns,and has better robustness and low er computational complexity compared w ith original WLD,u LBP,WLD + u LBP and other improved WLD methods,etc.
作者 郭仙草 石美红 李青 GUO Xiancao, SHI Meihong,LI Qing ( College of Computer Science, Xi' an Polytechnic University, Xi' an 710048, China)
出处 《计算机工程》 CAS CSCD 北大核心 2015年第4期210-216,共7页 Computer Engineering
基金 国家科技支撑计划基金资助项目(2014BAF07B01)
关键词 纹理分类 纹理特征 韦伯局部描述符 差分激励 正负梯度 局部二值模式 texture classification texture feature Weber Local Descriptor(WLD) differential excitation positive and negative gradient Local Binary Pattern(LBP)
作者简介 郭仙草(1990-),女,硕士研究生,主研方向:图形图像处理,模式识别; 石美红,教授; 李青,硕士研究生。
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