Drawing a beautiful painting is a dream of many people since childhood. In this paper, we propose a novel scheme, LINE ARTIST, to synthesize artistic style paintings with freehand sketch images, leveraging the power of deep learning and advanced algorithms. Our scheme includes three models. The Sketch Image Extraction (SIE) model is applied to generate the training data. It includes smoothing r...

Abstract Artistic style transfer is to render an image in the of another image, which a challenge problem both processing and arts. Deep neural networks are adopted artistic achieve remarkable success, such as AdaIN (adaptive instance normalization), WCT (whitening coloring transforms), MST (multimodal transfer), SEMST (structure-emphasized multimodal transfer). These algorithms modify content ...

BACKGROUND Art is a characteristic of mankind, which requires superior central nervous processing and integration of motor functions with visual information. At the present time, a significant amount of information related to neurobiological basis of artistic creation has been derived from neuro-radiological cognitive studies, which have revealed that subsequent to tissue destruction, the artis...

The essence of font style transfer is to move the features an image into a while maintaining font’s glyph structure. At present, generative adversarial networks based on convolutional neural play important role in generation. However, traditional that recognize images suffer from poor adaptability unknown changes, weak generalization abilities, and texture feature extractions. When structure ve...

In this work we investigate different avenues of improving the Neural Algorithm of Artistic Style [7]. While showing great results when transferring homogeneous and repetitive patterns, the original style representation often fails to capture more complex properties, like having separate styles of foreground and background. This leads to visual artifacts and undesirable textures appearing in un...

The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture th...

While artists demonstrate their individual styles through paintings and drawings, how to describe such artistic styles well selected visual features towards computerized analysis of the arts remains to be a challenging research problem. In this paper, we propose an integrated feature-based artistic descriptor with Monte Carlo Convex Hull (MCCH) feature selection model and support vector machine...

In this work we explore the method of style transfer presented in [1]. We first demonstrate the power of the suggested style space on a few examples. We then vary different hyper-parameters and program properties that were not discussed in [1], among which are the recognition network used, starting point of the gradient descent and different ways to partition style and content layers. We also g...

This note presents an extension to the neural artistic style transfer algorithm [2]. The original algorithm transforms an image to have the style of another given image. For example, a photograph can be transformed to have the style of a famous painting. Here we address a potential shortcoming of the original method: the algorithm transfers the colors of the original painting, which can alter t...