Expanding Convolutional Tsetlin Machine for Images with Lossless Binarization

2020

Master's thesis in Information- and communication technology (IKT590) Deep convolutional neural networks (CNN) is known to be efficient in image classification but non-interpretable. To overcome the black box nature of CNN a derivative of the Tsetlin automata, the convolutional Tsetlin machine (CTM) which is transparent and interpretable, was developed. As CTM handles binary inputs, it is important to transform the input images into binary form with minimum information loss so that the CTM can classify them correctly and efficiently. Currently, a relatively lossy mechanism, called adaptive Gaussian thresholding, was employed for binarization. To retain as much information as possible, in th…