Finding Image Features Associated with High Aesthetic Value by Machine Learning
Inhaltsverzeichnis
Referenz
Victor Ciesielski, Perry Barile, Karen Trist: Finding Image Features Associated with High Aesthetic Value by Machine Learning. In: EvoMUSART 2013, S. 47-58.
DOI
http://link.springer.com/10.1007/978-3-642-36955-1_5
Abstract
A major goal of evolutionary art is to get images of high aesthetic value. We assume that some features of images are associated with high aesthetic value and want to find them. We have taken two image databases that have been rated by humans, a photographic database and one of abstract images generated by evolutionary art software. We have computed 55 features for each database. We have extracted two categories of rankings, the lowest and the highest. Using feature extraction methods from machine learning we have identified the features most associated with differences. For the photographic images the key features are wavelet and texture features. For the abstract images the features are colour based features.
Extended Abstract
Bibtex
@incollection{
year={2013},
isbn={978-3-642-36954-4},
booktitle={Evolutionary and Biologically Inspired Music, Sound, Art and Design},
volume={7834},
series={Lecture Notes in Computer Science},
editor={Machado, Penousal and McDermott, James and Carballal, Adrian},
doi={10.1007/978-3-642-36955-1_5},
title={Finding Image Features Associated with High Aesthetic Value by Machine Learning},
url={http://dx.doi.org/10.1007/978-3-642-36955-1_5 http://de.evo-art.org/index.php?title=Finding_Image_Features_Associated_with_High_Aesthetic_Value_by_Machine_Learning },
publisher={Springer Berlin Heidelberg},
keywords={Evolutionary Art; Genetic Art; Feature Extraction; Feature Selection},
author={Ciesielski, Vic and Barile, Perry and Trist, Karen},
pages={47-58},
language={English}
}
Used References
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Links
Full Text
http://www.cs.rmit.edu.au/~vc/papers/evomusart13.pdf
