Hu moments and SVM does not work - c++

Question

I have come across one problem when trying to train data with SVM.
I get some different regions (set of connected pixels) from face images, and regions from eyes are very similar, so I want to use Hu moments for shape description and SVM for training.
But SVM does not work properly, method svm.predict evaluates afterwards everything as non-eye, moreover the same regions which were labeled and used in traning phase as eye, are evaluated as non-eye.
Feature data consists only of 7 Hu moments. I will post here some samples of source code in a moment, thanks in advance :)
Additional info:
input image:
http://i.stack.imgur.com/GyLO0.png
Setting up basic svm for 1 image:
int image_regions = 10;
Mat training_mat(image_regions ,7,CV_32FC1); // 7 hu moments
Mat labels(image_regions ,1,CV_32FC1); // for labels 1 (eye) and -1 (non eye)
// computing hu moments
Moments moments2=moments(croppedImage,false);
double hu[7];
HuMoments(moments2,hu);
// putting them into svm traning mat
for (int k=0;k<huCounter;k++)
training_mat.at<float>(counter,k) = hu[k]; // counter is current number of region
if (isEye(...))
{
labels.at<float>(counter,0)=1.0;
}
else
{
labels.at<float>(counter,0)=-1.0;
}
//I use the following:
CvSVM svm;
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 1e-6);
// ... do the above mentioned phase, and then:
svm.train(training_mat, labels, Mat(), Mat(), params);

Answer 1

I hope the following suggestions can help you…..
The simplest task is to use a clustering algorithm and try to cluster the data into two classes. If an algorithm like ‘k-means’ can do the job why make things complex by using SVM and Neural Nets. I suggest you use this technique because your feature vector dimension is of a very small size (7 Hu Moments) as well as your number of samples.
Perform feature Normalization (specified in point 4) to make sure the values fall in a limited range.
Check out “is your data really separable?” As your data is small, take a few samples from positive images and a few samples from negative images and plot the feature vectors. If you can visually see the difference surely any learning algorithm can do the job for you. As I said earlier simple tricks can do better than complex math.
Only if you then decide to use SVM you should know the following:
• As I can see from your code you are using a Linear SVM, may be your data is non-separable by a linear kernel. Try using some polynomial kernel or other kernels. There is one option bool CvSVM::train_auto in openCV just have a look.
• Try to check whether the feature vector values you are getting are proper values or not (make sure that they are not some garbage values).
• Also you can perform feature normalization “ZERO MEAN and UNIT VARIENCE” before you use it for training.
• Most importantly increase the number of images for training, both positively and negatively labeled.
• Last but not least SVM is not magic, at the end of the day it is just drawing a line between two sets of points. So don’t expect it to classify anything you give it as input.
If nothing works “Just improve your feature extraction technique”