I would like to ask a question about how to use SVM to classify images based on colors:
I have a dataset which contains 15 features extracted using simple image histogram includes mean, stddev, mode, and other statistics accompanied with a label in the same dataset.
How I can proceed from this dataset and how to change it to xml file store and load it in my Android app. Do I need to separate the dataset into training and label data, etcl.
I know there is lots of material in the website but a kind help from you will save my time.
Here the code I developed and I have this error: E/cv::error(): OpenCV Error: Assertion failed (samples.cols == var_count && samples.type() == CV_32F) in virtual float cv::ml::SVMImpl::predict(cv::InputArray, cv::OutputArray, int) const, file /Volumes/Linux/builds/master_pack-android/opencv/modules/ml/src/svm.cpp, line 1930
public static void SVMTBClassifier() SVMClassifier() {
MatOfDouble v_features = new MatOfDouble();
MatOfDouble vt_features = new MatOfDouble();
int trainingRow = 18;
int trainingCol = 18;
Mat trainingDataMat = new Mat(trainingRow, trainingCol, CvType.CV_32FC1);
int testingRow = 1;
int testingCol = 18;
Mat testingDataMat = new Mat(testingRow, testingCol, CvType.CV_32FC1);
featureVector = "featureVector";
double[][] trainingList = new double[trainingRow][trainingCol];
trainingList = readFromFile(featureVector, trainingRow, trainingCol);
for(int i=0;i<trainingRow;i++){
for(int j=0;j<trainingCol;j++) {
Log.i(TAG, "Data of data" + "[" + i + "," + j + "]" + " " + trainingList[i][j]);
trainingDataMat.put(i, j, trainingList[i][j]);
}
v_features.push_back(trainingDataMat.reshape(1,1));
}
featureVector = "label";
int[] labelList = new int[trainingRow];
labelList = readFromFile1(featureVector);
for(int i=0;i<trainingRow;i++){
Log.i(TAG, "Data of label " + "[" + i + "]" + " " + labelList[i]);
Mat labelsMat = new Mat(1, 1, CvType.CV_32SC1, new Scalar(labelList[i]));
trainingLabels.push_back(labelsMat);
}
Log.i(TAG,"Training...");
v_features.copyTo(trainingData);
trainingData.convertTo(trainingData, CvType.CV_32F);
trainingLabels.copyTo(classes);
classes.convertTo(classes, CvType.CV_32S);
classifier.setType(SVM.C_SVC);
classifier.setKernel(SVM.LINEAR);
classifier.setGamma(0.5);
classifier.setNu(0.5);
classifier.setC(1);
TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER,100,0.1);
classifier.setTermCriteria(criteria);
classifier.train(trainingData, Ml.ROW_SAMPLE, classes);
Log.i(TAG,"Done Training");
Log.i(TAG,"Testing...");
featureVector = "testVector";
double[][] testingList = new double[testingRow][testingCol];
testingList = readFromFile(featureVector, testingRow, testingCol);
for(int i=0;i<testingRow;i++) {
for (int j = 0; j < testingCol; j++) {
Log.i(TAG, "Data of test" + "[" + i + "," + j + "]" + " " + testingList[i][j]);
testingDataMat.put(i, j, testingList[i][j]);
}
vt_features.push_back(testingDataMat.reshape(1, 1));
}
vt_features.copyTo(testingData);
testingData.convertTo(testingData, CvType.CV_32F);
Log.i(TAG, "classification result" + classifier.predict(testingData));
}
