A study of neural artistic style transfer models and architectures for Indian art styles

Neural Style Transfer (NST) has been a widely researched topic as of late enabling new forms of image manipulation. Here we perform an extensive study on NST algorithms and extend the existing methods with custom modifications for application to Indian art styles. In this paper, we aim to provide a comprehensive analysis of various methods ranging from the seminal work of Gatys et al which demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style, to the state of the art image-to-image translation models which use Generative Adversarial Networks (GANs) to learn the mapping between two domain of images. We observe and infer based on the results produced by the models on which one could be a more suitable approach for Indian art styles, especially Tanjore paintings which are unique compared to the Western art styles. We then propose the method which is more suitable for the domain of Indian Art style along with custom architecture which includes an enhancement and evaluation module. We then present evaluation methods, both qualitative and quantitative which includes our proposed metric, to evaluate the results produced by the model.

Automated summary

This paper extensively studies neural style transfer algorithms and customizes them for Indian art styles. It analyzes various methods, from the seminal work of Gatys et al using CNNs to the state-of-the-art image-to-image translation models using GANs. Based on the results, the paper proposes a more suitable approach and custom architecture for Indian art styles, especially Tanjore paintings, and presents evaluation methods, including a proposed metric, to assess the model's results.