Feature Construction Using Genetic Programming for Classification of Images by Aesthetic Value
Inhaltsverzeichnis
Referenz
Andrew Bishop, Vic Ciesielski, Karen Trist: Feature Construction Using Genetic Programming for Classification of Images by Aesthetic Value. In: EvoMUSART 2014, S. 62-73.
DOI
http://link.springer.com/10.1007/978-3-662-44335-4_6
Abstract
Classification or rating of images according to their aesthetic quality has applications in areas such as image search, compression and photography. It requires the construction of features that are predictive of the aesthetic quality of an image. Constructing features manually for aesthetics prediction is challenging. We propose an approach to improve on manually designed features by constructing them using genetic programming and image processing operations implemented using OpenCV. We show that this approach can produce features that perform well. Classification accuracies of up to 81% on photographs and 92% on computationally generated images have been achieved. Both of these results significantly improve on existing manually designed features.
Extended Abstract
Bibtex
@incollection{
year={2014},
isbn={978-3-662-44334-7},
booktitle={Evolutionary and Biologically Inspired Music, Sound, Art and Design},
volume={8601},
series={Lecture Notes in Computer Science},
editor={Romero, Juan and McDermott, James and Correia, João},
doi={10.1007/978-3-662-44335-4_6},
title={Feature Construction Using Genetic Programming for Classification of Images by Aesthetic Value},
url={http://dx.doi.org/10.1007/978-3-662-44335-4_6 http://de.evo-art.org/index.php?title=Feature_Construction_Using_Genetic_Programming_for_Classification_of_Images_by_Aesthetic_Value },
publisher={Springer Berlin Heidelberg},
keywords={Genetic Programming; Feature Construction; Image Aesthetics},
author={Bishop, Andrew and Ciesielski, Vic and Trist, Karen},
pages={62-73},
language={English}
}
Used References
Ciesielski, V., Barile, P., Trist, K.: Finding image features associated with high aesthetic value by machine learning. In: Machado, P., McDermott, J., Carballal, A. (eds.) EvoMUSART 2013. LNCS, vol. 7834, pp. 47–58. Springer, Heidelberg (2013)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Studying aesthetics in photographic images using a computational approach. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 288–301. Springer, Heidelberg (2006)
Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: Fitzgibbon, A., Taylor, C.J., LeCun, Y. (eds.) CVPR (1), pp. 419–426. IEEE Computer Society (2006)
Krawiec, K.: Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genetic Programming and Evolvable Machines 3(4), 329–343 (2002)
Krawiec, K., Bhanu, B.: Coevolution and linear genetic programming for visual learning. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 332–343. Springer, Heidelberg (2003)
Roberts, M.E., Claridge, E.: Cooperative coevolution of image feature construction and object detection. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 902–911. Springer, Heidelberg (2004)
Zhang, M., Ciesielski, V.: Genetic programming for multiple class object detection. In: Foo, N.Y. (ed.) AI 1999. LNCS, vol. 1747, pp. 180–192. Springer, Heidelberg (1999)
Zhang, M., Wong, P.: Genetic programming for medical classification: a program simplification approach. Genetic Programming and Evolvable Machines 9(3), 229–255 (2008)
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