You need to put in train_data the pixel values from your training image, and in responses an index corresponding to the class of this pixel (e.g. 1 for class skin, 0 for class non-skin). var_idx and sample_idx can be left as is, they are used to mask out some of the descriptors or samples in your training set. Set update to true/false depending on wether you get all the descriptors (all the pixels of all your training images) at once in case you can let it to false, or you process your training images incrementally (which might be better for memory issues), in which case you need to update your model.
Let me clarify you with the code (not checked, and using the C++ interface to OpenCV which I strongly recommand instead of the old C)
int main(int argc, char **argv)
{
CvNormalBaseClassifier classifier;
for (int i = 0; i < argc; ++i) {
cv::Mat image = // read in your training image, say cv::imread(argv[i]);
// read your mask image
cv::Mat mask = ...
cv::Mat response = mask == CV_RGB(255,0,0); // little trick: you said red pixels in your mask correspond to skin, so pixels in responses are set to 1 if corresponding pixel in mask is red, 0 otherwise.
cv::Mat responseInt;
response.convertTo(responsesInt, CV_32S); // train expects a matrix of integers
image = image.reshape(0, image.rows*image.cols); // little trick number 2 convert your width x height, N channel image into a witdth*height row matrix by N columns, as each pixel should be considere as a training sample.
responsesInt = responsesInt.reshape(0, image.rows*image.cols); // the same, image and responses have the same number of rows (w*h).
classifier.train(image, responsesInt, 0, 0, true);
}
