在SVM opencv c ++中标记数据 [英] labeling data in SVM opencv c++

问题描述

我试图在opencv中实现SVM，使用SIFT提取的功能。我已经提取了2个不同的对象的特征（每个对象有10个不同的图像的功能，总共我有一个对象超过3000个功能），我把这些功能放在一个文件（yaml文件）..

I'm trying to implement SVM in opencv for features that I have extracted features by using SIFT. I have extracted features for 2 different objects (each object has features of 10 different images which in total I got more than 3000 features for one object) and I put those features in one file (yaml file)..

我的问题是：我不知道如何标记他们？所以我需要标记这两个文件（如我所说每个文件是yaml的类型，它包含矩阵3260 * 128和第二个对象的第二个yaml文件是3349 * 128）...

My problem is: I don't know how to label them? so I need to label these two files (as I said each file is the type of yaml and it contains matrix 3260*128 and the second yaml file for the second object is 3349*128)...

所以，请帮助我告诉我如何标记这些文件，以便训练他们...我使用openCV c ++ ..顺便说一下，openCV代码为SVM是基于LIBSVM

So please help me to show me how to label these files in order to train them later on... I'm using openCV c++.. by the way, the openCV code for SVM is based on LIBSVM

感谢您的进阶

推荐答案

正确，每行代表一个样例，你可以做什么类似于lakesh建议的：

Assume you get your matrix correctly, and each row represents one sample, what you can do is similar to what lakesh suggested:

Cv::Mat anger, disgust;
// Load the data into anger and disgust
...
// Make sure anger.cols == disgust.cols 
// Combine your features from different classes into one big matrix
int numPostives = anger.rows, numNegatives = disgust.rows;
int numSamples = numPostives+numNegatives;
int featureSize = anger.cols;
cv::Mat data(numSamples, featureSize, CV_32FC1) // Assume your anger matrix is in float type
cv::Mat positiveData = data.rowRange(0, numPostives);
cv::Mat negativeData = data.rowRange(numPostives, numSamples);
anger.copyTo(positiveData);
disgust.copyTo(negativeData);
// Create label matrix according to the big feature matrix
cv::Mat labels(numSamples, 1, CV_32SC1);
labels.rowRange(0, numPositives).setTo(cv::Scalar_<int>(1));
labels.rowRange(numPositives, numSamples).setTo(cv::Scalar_<int>(-1));
// Finally, train your model
cv::SVM model;
model.train(data, labels, cv::Mat(), cv::Mat(), cv::SVMParams());

希望这有帮助。

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