Construction of texture features

One well-known and effective method used for computationally efficient texture classification is the use of statistical information on 3times3 pixel blocks such as local binary patterns (LBP). However, there has been negligible research on sizes of pixel blocks beyond 3times3 while using the histogram approach. Specifically, larger or non-square features might give better classification results. Our proposed method constructs features, with arbitrary size and shape, that will give the best results for classifying a specific texture, and still keeping the feature vectors as short as possible. In this work, we show the selected features and the performance of our method with a minimum distance classifier and with a neural network and provide quantitative comparisons to the 3times3 block method on both the Brodatz and Ponce texture databases.