I have 14 classes of images - these are sampled from a kinect video of 14 objects. Using HOG for feature extraction and SVM for classification. There are 20 training samples for each class. I have also tried KAZE feature extractor and Random Forest, but the same class is predicted for all. what am I doing wrong?
These are the parameters used for SVM ////create and train the svm //Ptr<ml::svm> svm = SVM::create(); //svm->setKernel(SVM::RBF); //svm->setType(SVM::C_SVC); ////Ptr<ml::paramgrid> nogrid = ml::ParamGrid::create(0, 0, 0);//no need nu, coeff0 or p //auto td = TrainData::create(mat, ROW_SAMPLE, labels); //ParamGrid Cgrid = SVM::getDefaultGrid(SVM::C); //ParamGrid pGrid = SVM::getDefaultGrid(SVM::P); //ParamGrid gammaGrid = SVM::getDefaultGrid(SVM::GAMMA); //ParamGrid nuGrid = SVM::getDefaultGrid(0); //ParamGrid coeffGrid = SVM::getDefaultGrid(SVM::COEF); //ParamGrid degreeGrid = SVM::getDefaultGrid(0);
//svm->trainAuto(td,30,Cgrid,gammaGrid,pGrid,nuGrid,coeffGrid,degreeGrid,true);
Thanks for any help