Enhanced Artistic Image Style Transfer Using Convolutional Neural Networks

Abstract

Have you ever wondered how applications like Prisma and other artistic applications work, we input the image from the camera roll to application software and then we select the design to extract the image with the selected artistic design which is a completely different from the initial style? In the context of Artificial Intelligence this is also called style transfer. We use convolutional neural networks in the artistic style transfer, style transfer basically transfers the images by mixing it style of the another images. CNN is sub-branch of neural networks which is very useful in classification of the images it also recognizes the images, they spot the objects in the images with the human faces which empower the automated robots. We use 64,128,512 filters to change the artistic feature of the image. VGG is visual geometry group which can give most success arte of clustering of 93% with only 7% of error which also can be rectified by taking certain measurements. We recreate the images whose features mix the selected convolution layer of the input content image. By mixing the image with selected convolution layer we can construct the beautiful artistic image.

Keywords: Neural style, Convolutional neural system, Image, Framework, Organic vision, Portrayal