Classification of Textures Using Convolutional Neural Networks

This Master's thesis has concerned the segmentation and classification of background textures in images. In order to segment the images we have used the SLIC algorithm to create superpixels. These are a sort of over segmentation of the image where pixels close to each other and similar in colour are considered to be the same texture. All superpixels were then classified using a convolutional neural network which was trained as a part of this thesis. As this network had about 30% errors a second stage was added to the classification program, a spatial bias. The first attempt at this spatial bias used the neighbouring superpixels' classification in order to make the image more homogeneous. Secondly, as a comparison, a neural network was also trained as the spatial bias. When using the neural network followed by the neural network for spatial biases the errors decreased to a little more than 10%, while the ordinary spatial bias only decreased error by a few percent. Even though the best performing network had a low error rate we were not able to replicate these results with unknown images as the network most likely severely overfitted to the relatively small training set. The method in it self showed potential and with more training data and possibly smaller superpixels we could get more consistent results between training images and other unknown data