Decision trees trained using greedy recursive partitioning have existed for decades in machine learning and statistics. However, they have suffered from two critical limitations: they do not optimize a global objective function of the tree parameters, and they use a weak form of partitioning based on a single feature. Recent advances, specifically the tree alternating optimization algorithm, have removed those limitations. This makes it possible to use more powerful types of trees, for example using hyperplane splits, and to apply them to new tasks beyond regression and classification. We illustrate this by showing how oblique decision trees and forests can function as an image model for various applications, such as image segmentation, compression or manipulation for aesthetic effects.