利用SVM或者其他机器学习算法进行分类识别 LBP
代码说明:
(1)计算图像中每个像素点的LBP模式(等价模式,或者旋转不变+等价模式)。 (2)然后计算每个cell的LBP特征值直方图,然后对该直方图进行归一化处理(每个cell中,对于每个bin,h[i]/=sum,sum就是一副图像中所有等价类的个数)。 (3)最后将得到的每个cell的统计直方图进行连接成为一个特征向量,也就是整幅图的LBP纹理特征向量; 然后便可利用SVM或者其他机器学习算法进行分类识别了。((1) calculate the LBP pattern of each pixel in the image (equivalent mode, or rotation invariant + equivalent mode). (2) then the LBP eigenvalue histogram of each cell is calculated, and then the histogram is normalized (for each cell, for each bin, h[i]/=sum, sum is the number of all the equivalent classes in a pair of images). (3) finally, the statistical histogram of each cell is connected into a feature vector, that is, the LBP texture feature vector of the whole picture. Then, SVM or other machine learning algorithms can be used for classification and recognition.)
文件列表:
getmapping.m, 2762 , 2018-04-20
LBP.m, 6068 , 2018-04-20
lbptest.m, 1683 , 2018-04-20
test1.bmp, 66614 , 2018-04-20
test2.bmp, 66614 , 2018-04-20
test3.bmp, 66614 , 2018-04-20
test4.bmp, 66614 , 2018-04-20
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