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利用SVM或者其他机器学习算法进行分类识别 LBP

于 2020-07-01 发布文件大小:67KB

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下载积分: 1 下载次数: 30

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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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