Evolutionary Image Composition Using Feature Covariance Matrices
Aneta Neumann, Zygmunt L. Szpak, Wojciech Chojnacki, Frank Neumann
TL;DR
This paper introduces a flexible evolutionary algorithm that creates new images by combining features from existing images using feature covariance matrices, enabling targeted and aesthetically pleasing compositions.
Contribution
The paper presents a novel evolutionary approach utilizing feature covariance matrices for image composition, allowing flexible feature integration and targeted region editing.
Findings
Generated images are aesthetically pleasing.
Method effectively targets specific image regions.
Flexible feature incorporation enhances creative possibilities.
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.
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Taxonomy
TopicsAesthetic Perception and Analysis