ScribGen: generating scribble art through metaheuristics

Abstract

Scribble art, arising from chaos and randomness, remains one of the exceptionally attractive forms of art. Many works bridge the gap between sketches and images, but few translate images into meaningful chaotic expressions. While deep generative networks are known for understanding images, their ability to induce scribble drawings is under-explored. Unlike GAN-based approaches that generate line drawings, sketches, and contours, our work uses metaheuristics to produce scribble art from images. We extensively analyse various metaheuristic algorithms, demonstrating their optimal balance between creativity and computational efficiency. They offer better adaptability and accuracy than state-of-the-art deep generative models for image-to-scribble generation.