物体识别中经常遇到多分类器问题,svm是比较成熟和直接的想法。一般来说使用svm作为多分类器主要有以下思路:
一对多(one-vs-all)。训练时依次将目标类别作为正样本,其余样本作为负样本,以此训练n个svm。这个在Andrew Ng的Machine leaning的课上介绍过。
缺点:因为训练集是1:N的情况,存在较大的bias,不是特别实用。
一对一(one-vs-one)。训练时,任意两类样本之间训练一个svm,则n类别,训练出(n-1)n/2个svm。在runtime时,对一个未知样本分类,则使用投票的法方法。libsvm即使用的该种方法。
缺点:类别多的时候,(n-1)n/2个支持向量机,计算代价大。
层次支持向量机。首先将所有类别分类为两个子类,再将子类进一步划分为两个子类,直到单独子类为止。好像一棵树耶。具体请参考:刘志刚, 李德仁, 秦前清, 等. 支持向量机在多类分类问题中的推广[J]. 2004.
DAG-SVMS。由Platt提出的决策导向的循环图DDAG导出的,是针对“一对一”SVMS存在误分、拒分现象提出的。请参考论文
#include
#include
#include "opencv2/imgcodecs.hpp"
#include
#include
using namespace cv;
using namespace cv::ml;
Vec3b getRandomColor(){
RNG rng(clock());
return Vec3b(rng.next() % 255, rng.next() % 255, rng.next() % 255);
}
int main(int, char**)
{
// Data for visual representation
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3);
// Set up training data
int labels[4] = {1, 2, 3, 4};
float trainingData[4][2] = { {100, 10}, {10, 500}, {500, 10}, {500, 500} };
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
Mat labelsMat(4, 1, CV_32SC1, labels);
// Train the SVM
//! [init]
Ptr svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::POLY);
svm->setDegree(1.0);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
//! [init]
//! [train]
// svm->train(trainingDataMat, ROW_SAMPLE, labelsMat);
// Ptr auto_train_data = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
// svm->trainAuto(auto_train_data);
svm->train(trainingDataMat, ROW_SAMPLE, labelsMat);
//! [train]
// Show the decision regions given by the SVM
//! [show]
Vec3b green(0,255,0), blue (255,0,0), red(0,0,255),yellow(0,255,255);
for (int i = 0; i < image.rows; ++i){
for (int j = 0; j < image.cols; ++j){
Mat sampleMat = (Mat_<float>(1,2) << j,i);
float response = svm->predict(sampleMat);
double ratio = 0.5;
if (response == 1)
image.at(i,j) = green*ratio;
else if (response == 2)
image.at(i,j) = blue*ratio;
else if(response == 3){
image.at(i,j) = red*ratio;
}else if(response == 4){
image.at(i,j) = yellow*ratio;
}
}
}
int thickness = -1;
int lineType = 8;
circle( image, Point(100, 10), 5, Scalar( 0,255,0), thickness, lineType );
circle( image, Point(10, 500), 5, Scalar(255,0,0), thickness, lineType );
circle( image, Point(500, 10), 5, Scalar(0,0,255), thickness, lineType );
circle( image, Point( 500, 500), 5, Scalar(0,255,255), thickness, lineType );
thickness = 2;
lineType = 8;
Mat sv = svm->getSupportVectors();
std::cout << sv << std::endl;
for (int i = 0; i < sv.rows; ++i){
const float* v = sv.ptr<float>(i);
circle( image, Point( (int) v[0], (int) v[1]), 6, CV_RGB(128, 128, 128), 2);
}
imwrite("result.png", image); // save the image
imshow("SVM Simple Example", image); // show it to the user
waitKey(0);
}
注意点:
使用RBF核或者使用autotrain,参数选择十分重要。不行你试试哟!!!