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改进svm

于 2021-03-06 发布
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下载积分: 1 下载次数: 10
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代码说明：

说明：  phog方法提取图像特征，svm支持向量机进行分类，分别有GA遗传算法和PSO粒子群优化算法进行寻优。(Phog method extracted image features, SVM support vector machine classification, respectively, GA genetic algorithm and PSO particle swarm optimization algorithm for optimization.)

文件列表：

1_30.jpg, 7107 , 2020-04-21
1_30.jpg.txt, 4237 , 2020-05-24
2_30.jpg, 51795 , 2020-04-21
3_30.jpg, 37510 , 2020-04-21
4_30.jpg, 20977 , 2020-04-21
5_30.jpg, 66186 , 2020-04-21
Accuracy_GA_SVM.m, 1232 , 2020-05-09
Accuracy_PSO_SVM.m, 1233 , 2020-05-23
Accuracy_SVM.m, 1225 , 2020-05-23
anna_binMatrix.m, 1455 , 2020-05-04
anna_phog.m, 1818 , 2020-05-04
anna_phog_demo.m, 170 , 2020-05-24
anna_phogDescriptor.m, 1215 , 2020-05-04
Demo-image, 0 , 2020-05-09
Demo-image\0.jpg, 156403 , 2015-05-27
Demo-image\1.jpg, 91161 , 2015-05-27
Demo-image\10.jpg, 4101 , 2013-03-24
Demo-image\11.jpg, 156581 , 2015-05-27
Demo-image\12.jpg, 22357 , 2015-05-27
Demo-image\13.jpg, 11730 , 2013-03-24
Demo-image\14.jpg, 15632 , 2013-03-24
Demo-image\2.jpg, 155563 , 2015-05-27
Demo-image\3.jpg, 52060 , 2015-05-27
Demo-image\4.jpg, 127184 , 2015-05-27
Demo-image\5.jpg, 13811 , 2013-03-24
Demo-image\6.jpg, 16727 , 2013-03-24
Demo-image\7.jpg, 151037 , 2015-05-27
Demo-image\8.jpg, 138947 , 2015-05-27
Demo-image\9.jpg, 14759 , 2013-03-24
Demo-image\test, 0 , 2020-05-09
Demo-image\test\0.jpg, 52667 , 2020-04-21
Demo-image\test\0.jpg.txt, 5970 , 2020-04-23
Demo-image\test\1.jpg, 45562 , 2020-04-21
Demo-image\test\1.jpg.txt, 4633 , 2020-04-23
Demo-image\test\10.jpg, 56504 , 2020-04-21
Demo-image\test\10.jpg.txt, 4671 , 2020-04-23
Demo-image\test\11.jpg, 22861 , 2020-04-21
Demo-image\test\11.jpg.txt, 2884 , 2020-04-23
Demo-image\test\12.jpg, 13102 , 2020-04-21
Demo-image\test\12.jpg.txt, 5532 , 2020-04-23
Demo-image\test\13.jpg, 14343 , 2020-04-21
Demo-image\test\13.jpg.txt, 5435 , 2020-04-23
Demo-image\test\14.jpg, 19155 , 2020-04-23
Demo-image\test\14.jpg.txt, 6320 , 2020-04-23
Demo-image\test\2.jpg, 20783 , 2020-04-21
Demo-image\test\2.jpg.txt, 4115 , 2020-04-23
Demo-image\test\3.jpg, 53974 , 2020-04-21
Demo-image\test\3.jpg.txt, 5542 , 2020-04-23
Demo-image\test\4.jpg, 13718 , 2020-04-21
Demo-image\test\4.jpg.txt, 5774 , 2020-04-23
Demo-image\test\5.jpg, 9475 , 2020-04-21
Demo-image\test\5.jpg.txt, 4666 , 2020-04-23
Demo-image\test\6.jpg, 47424 , 2020-04-21
Demo-image\test\6.jpg.txt, 5205 , 2020-04-23
Demo-image\test\7.jpg, 34818 , 2020-04-21
Demo-image\test\7.jpg.txt, 5374 , 2020-04-23
Demo-image\test\8.jpg, 11194 , 2020-04-21
Demo-image\test\8.jpg.txt, 5287 , 2020-04-23
Demo-image\test\9.jpg, 3859 , 2020-04-21
Demo-image\test\9.jpg.txt, 2979 , 2020-04-23
Demo-image\u=2140437051,2007055591&fm=26&gp=0.jpg, 8892 , 2020-05-09
Demo.m, 1244 , 2020-04-23
GA_SVM_Demo.m, 973 , 2020-05-06
gaSVMcgForClass.m, 4351 , 2020-04-23
PSO_SVM_Demo.m, 807 , 2020-05-23
psoSVMcgForClass.m, 5928 , 2020-05-09
SVM_outputPicture.m, 2256 , 2020-05-24
test, 0 , 2020-05-09
test\1_01.jpg, 47431 , 2020-04-21
test\1_01.jpg.txt, 5465 , 2020-05-23
test\1_02.jpg, 11212 , 2020-04-21
test\1_02.jpg.txt, 4858 , 2020-05-23
test\1_03.jpg, 7350 , 2020-04-21
test\1_03.jpg.txt, 4096 , 2020-05-23
test\1_04.jpg, 12249 , 2020-04-21
test\1_04.jpg.txt, 5753 , 2020-05-23
test\1_05.jpg, 11837 , 2020-04-21
test\1_05.jpg.txt, 6228 , 2020-05-23
test\1_06.jpg, 10258 , 2020-04-21
test\1_06.jpg.txt, 5186 , 2020-05-23
test\1_07.jpg, 10492 , 2020-04-21
test\1_07.jpg.txt, 5177 , 2020-05-23
test\1_08.jpg, 10399 , 2020-04-21
test\1_08.jpg.txt, 4980 , 2020-05-23
test\1_09.jpg, 52667 , 2020-04-21
test\1_09.jpg.txt, 5970 , 2020-05-23
test\1_10.jpg, 50159 , 2020-04-21
test\1_10.jpg.txt, 5165 , 2020-05-23
test\2_01.jpg, 43136 , 2020-04-21
test\2_01.jpg.txt, 5450 , 2020-05-23
test\2_02.jpg, 41310 , 2020-04-21
test\2_02.jpg.txt, 5863 , 2020-05-23
test\2_03.jpg, 41105 , 2020-04-21
test\2_03.jpg.txt, 5690 , 2020-05-23
test\2_04.jpg, 64072 , 2020-04-21
test\2_04.jpg.txt, 6231 , 2020-05-23
test\2_05.jpg, 46495 , 2020-04-21
test\2_05.jpg.txt, 5142 , 2020-05-23
test\2_06.jpg, 9630 , 2020-04-21
test\2_06.jpg.txt, 4856 , 2020-05-23

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    1.本程序功能包含显示直方图，OTSU的单值分割，OTSU的二值分割,OTSU的三值分割 2.在OTSU的分割时，通常情况下我们是通过类间方差得到一个分割阈值，但是有时候我们需要得到各灰度级别的目标，那么采取多值分割是 很必要的，通过改进得到OTSU的多值分割方法 3.本程序另外还有显示直方图，计算程序运行时间，处理结果以新的窗口显示等细节，这部分适合MFC初学者。(1. This procedure shows the histogram feature, OTSU single segmentation, OTSU binary segmentation, OTSU three segmentation 2. OTSU segmentation, it is usually the case we are through the between-cluster variance by a partition threshold , but sometimes we need to be the gray level of the target, then the multi-segmentation is necessary, through improved access to multi-OTSU segmentation methods 3. This procedure in addition to show the histogram to calculate run time, treatment the results of a new window display the details of this part of MFC for beginners.)
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    积分：1
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    2010-05-07 14:51:01下载
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