Revisiting Artistic Style Transfer For Data Augmentation in A Real-Case Scenario

Since a few years and the advent of convolutional neural networks, algorithms for artistic style transfer between images have developed considerably. However, these methods require a relatively long training phase in order to succeed. This is why non-learning image processing approaches recently strove to propose patch-based algorithms able to aesthetically compete with neural methods. This paper goes one step further in this direction by introducing a new patch-based method for style transfer, using a constrained multi-scale version of the fast approximate nearest-neighbor algorithm PatchMatch, enforcing uniform sampling of style feature-patch. Our method also aims to mix the patch-based and neural paradigms by enabling the embedding of image patches in the feature space of the VGG-16 network.