New Approach for the Aesthetic Improvement of Images Through the Combination of Convolutional Neural Networks and Evolutionary Algorithms

• Juan Abascal ^[1] ; Patricio, Miguel A. ^[1] ; Antonio Berlanga ^[1] ; Molina, José M. ^[1]
  1. [1] Universidad Carlos III de Madrid

     Universidad Carlos III de Madrid

     Madrid, España

     
• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 229-240
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • Programs for aesthetic improvements of the images have been one of the applications more widely in the last years so much from the commercial point of view like the private one. The improvement of images has been made through the application of different filters that transform the original image into another whose aesthetics have been improved. In this work a new approach for the automatic improvement of the aesthetics of images is presented. This approach uses a Convolutional Neural Network (CNN) network trained with the AVA photography data set, which contains around 255,000 images that are valued by amateur photographers. Once trained, we will have the ability to assess an image in terms of its aesthetic characteristics. Through an evolutionary differential algorithm, an optimization process will be carried out in order to find the parameters of a set of filters that improve the aesthetics of the original image. As a fitness function the trained CNN will be used. At the end of the experimentation, the viability of this methodology is presented, analyzing the convergence capacity and some visual results.