摘要
提出了一种改进的基本图像特征(basic image feature,BIF)直方图纹理分类算法。首先在4个尺度上分别确定图像中每个像素点对应的BIF,然后在每个尺度上分别提取6维直方图特征及3维高阶统计特征共36维特征,最后使用支持向量机(support vector machine,SVM)作分类器对实验图像进行训练和分类。实验表明,所提方法降低了算法的计算复杂度和运行时间,对噪声有较好的鲁棒性。
An improved method of basic image features （BIF） histogram is proposed. Firstly, correspond ing BIF of every pixel in the image is respectively computed in four scales. Secondly, six histogram features and three high order statistical features are extracted respectively in every scale. Finally, the support vector machine （SVM） is applied for training and classifying the texture images. Experiment results show that the proposed method can reduce the computing complexity and consuming time, meanwhile, it is robust to noise.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2012年第6期1272-1277,共6页
Systems Engineering and Electronics
基金
国家自然科学基金(60970142
60903221)资助课题
关键词
图像处理
纹理分类
基本图像特征
最近邻算法
支持向量机
image processing
texture classification
basic image feature （BIF） nearest neighbor algorithm
support vector machine （SVM）
