Classifying Images of Two-Dimensional Fractional Brownian Motion through Deep Learning and Its Applications

Abstract

Two-dimensional fractional Brownian motion (2D FBM) is an effective model for describing natural scenes and medical images. Essentially, it is characterized by the Hurst exponent (H) or its corresponding fractal dimension (D). For optimal accuracy, we can use the maximum likelihood estimator (MLE) to compute the value. However, its computational cost is much higher than other low-accuracy estimators. Therefore, we propose a feasible deep-learning model and find out some promising pretrained models to classify the Hurst exponent efficiently and effectively. For evaluating the efficacy of deep learning models, two types of 2D FBM images were generated—11 classes and 21 classes of Hurst exponents. For comparison, we first used the efficient MLE to estimate the Hurst exponent of each image and then classified them through machine learning models. On the other hand, we used deep learning models to train and classify all images. Experimental results show that our proposed model and some pretrained models are much higher in accuracy than machine learning models for estimates from the efficient MLE. When applied, deep learning models take much lower computational time than the efficient MLE. Therefore, for accuracy and efficiency, we can use deep learning models to replace the role of the efficient MLE in the future.