Evolutionary Image Composition Using Feature Covariance Matrices

Aneta Neumann; Zygmunt L. Szpak; Wojciech Chojnacki; Frank Neumann

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arXiv · 1703.03773

Evolutionary Image Composition Using Feature Covariance Matrices

Aneta Neumann·Zygmunt L. Szpak·Wojciech Chojnacki·Frank Neumann

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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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BibTeX RIS

Aneta Neumann, Zygmunt L. Szpak, Wojciech Chojnacki, Frank Neumann. 2017-03-10. Evolutionary Image Composition Using Feature Covariance Matrices. https://arxiv.org/abs/1703.03773

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