I m a beginner in the field of pattern recognition and computer vision. I m working on a project right now to classify t-shirt patterns into three categories i.e. solids, stripes and checks. I have close up training images of the t-shirt images. A sample shirt image looks like this http://img6a.flixcart.com/image/shirt/p/y/p/jwffsc501a2-john-players-40-700x700-imadtx2cpg6zyzmf.jpegI have looked at a bank of gabor filter features, but they are computationally expensive. It would of great help if someone could point me out in the general direction for working forward. Any help is appreciated.EDIT - I found the solution in D.W.'s answer below, though my solution is not very good. I m classifying solid patterns by counting the number of line segments in the image. If they fall below a certain number, i m classifying them as solid. If not, i further classify them into stripes or checkered using HoG features and a linear SVM. The accuracy achieved was around 91%. It was a little low due to some misclassifed samples in the training set.
Extracting features for texture classification
machine learning;image processing;pattern recognition
I suggest you try looking at some different simple features and filters, to look for ones that might be useful in distinguishing the shirts, and then feed these feature values into a machine learning algorithm.  You can use a training set of images that you've hand-annotated to train the machine learning model.For instance, one natural feature would be something that looks for short horizontal lines or vertical lines.  You could use @Aky's suggestions, and then count the number of locations in the image where such a line has been detected.  You could also try filtering your algorithm with an edge detector.