Abstract
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.
This work was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), references TEC2017-88048-C2-2-R, RTC-2016-5595-2, RTC-2016-5191-8 and RTC-2016-5059-8.
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Abascal, J., Patricio, M.A., Berlanga, A., Molina, J.M. (2019). New Approach for the Aesthetic Improvement of Images Through the Combination of Convolutional Neural Networks and Evolutionary Algorithms. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_20
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