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Assessing Aesthetics of Generated Abstract Images Using Correlation Structure
Can we generate abstract aesthetic images without bias from natural or human selected image corpi? Are aesthetic images singled out in their correlation functions? In this paper we give answers to these and more questions. We generate images using compositional pattern-producing networks with random weights and varying architecture. We demonstrate that even with the randomly selected weights the correlation functions remain largely determined by the network architecture. In a controlled experiment, human subjects picked aesthetic images out of a large dataset of all generated images. Statistical analysis reveals that the correlation function is indeed different for aesthetic images.
@inproceedings{item_3275070, title = {{Assessing Aesthetics of Generated Abstract Images Using Correlation Structure}}, booktitle = {{2019 IEEE Symposium Series on Computational Intelligence (SSCI)}}, abstract = {Can we generate abstract aesthetic images without bias from natural or human selected image corpi? Are aesthetic images singled out in their correlation functions? In this paper we give answers to these and more questions. We generate images using compositional pattern-producing networks with random weights and varying architecture. We demonstrate that even with the randomly selected weights the correlation functions remain largely determined by the network architecture. In a controlled experiment, human subjects picked aesthetic images out of a large dataset of all generated images. Statistical analysis reveals that the correlation function is indeed different for aesthetic images.}, pages = {306--313}, publisher = {IEEE}, address = {Xiamen, China}, year = {2019}, note = {IEEE Symposium Series on Computational Intelligence (SSCI 2019)}, slug = {item_3275070}, author = {Khajehabdollahi, S and Martius, G. and Levina, A} }