Evolutionary Image Composition Using Feature Covariance Matrices
This work addresses image composition for artistic applications, but it is incremental as it builds on existing evolutionary methods with a new fitness component.
The paper tackled the problem of composing new images from existing ones while preserving salient features, using evolutionary algorithms with a fitness function based on feature covariance matrices, resulting in a spectrum of aesthetically pleasing images.
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.