OpenCV--SVM多分类问题

article/2025/9/7 23:03:09

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

简单示例

#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/highgui.hpp>
#include <opencv2/ml.hpp>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 representationint width = 512, height = 512;Mat image = Mat::zeros(height, width, CV_8UC3);// Set up training dataint 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 = 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<TrainData> 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<Vec3b>(i,j)  = green*ratio;else if (response == 2)image.at<Vec3b>(i,j)  = blue*ratio;else if(response == 3){image.at<Vec3b>(i,j) = red*ratio;}else if(response == 4){image.at<Vec3b>(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 imageimshow("SVM Simple Example", image); // show it to the userwaitKey(0);
}