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I think this is a classical texture classification problem, so a classical method should work. These are fast and robust methods. I think you were on the right track with the Gabor filters, only the size of the filters wasn't well chosen.
Another good and fast texture descriptor are the Haralick features. It isn't part of OpenCV, but implementing it is very easy.
If your texture has a typical color, you can also use the mean H,S,V values as features.
For the classification, I prefer to use the SVM for this kind of problem.
So the algorithm again: Take a sliding window; compute the Haralick features on the window. Or run Gabor filters on the image. Use the Haralick features or the results of the Gabor filter for the current pixel as input vector for an SVM, it will give you the texture class for the current pixel.
Use images of pure texture to train the SVM.
