基于多尺度LBP金字塔特征的分类算法

摘要

In order to effectively solve the rotation changes, lighting changes and scale changes in the problem of image classification difficulties,a novel image classification algorithm based on multi-scales Local Binary Pattem(LBP) pyramid feature is proposed.It builds a multi-scales LBP pyramid to extract image texture features.It makes up the multi-scales LBP pyramid histograms by the extracted features.The dimensionality of multi-scales LBP pyramid histograms is reduced by kmeans clustering for image classification.The analysis of experimental results proves that the proposed algorithm has better discriminative power and image description ability.Furthermore, aiming at the drawback of convention weighting scheme based on the binary feature matching, it also proposes a new weighting scheme named multi-dominant feature weighting.Experimental results show this method actually improves the performance of classification.%为有效解决旋转变化、光照变化和尺度变化等图像的分类问题,提出一种基于多尺度局部二元模式(LBP)金字塔特征的图像分类算法.通过多尺度LBP金字塔提取各尺度的图像纹理特征,建立图像的多尺度LBP金字塔直方图,并将其作为图像特征向最,采用K-means方法对该特征向量进行降维,以用于图像分类.同时,针对传统二进制权值分布方法对噪声敏感的缺点,提出一种多端权值分布方法.实验结果表明,多尺度LBP金字塔方法具有较好的可鉴别性及图像描述能力,而多端权值分布法也能提高图像的分类精度.