Evolutionary Image Composition Using Feature Covariance Matrices
Abstract
Evolutionary algorithms have recently been used to create a wide range of artistic work. In this paper, we propose a new approach for the composition of new images from existing ones, that retain some salient features of the original images. We introduce evolutionary algorithms that create new images based on a fitness function that incorporates feature covariance matrices associated with different parts of the images. This approach is very flexible in that it can work with a wide range of features and enables targeting specific regions in the images. For the creation of the new images, we propose a population-based evolutionary algorithm with mutation and crossover operators based on random walks. Our experimental results reveal a spectrum of aesthetically pleasing images that can be obtained with the aid of our evolutionary process.
Cite
@article{arxiv.1703.03773,
title = {Evolutionary Image Composition Using Feature Covariance Matrices},
author = {Aneta Neumann and Zygmunt L. Szpak and Wojciech Chojnacki and Frank Neumann},
journal= {arXiv preprint arXiv:1703.03773},
year = {2017}
}