Abstract
Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty. Judging beauty and other aesthetic qualities of photographs is a highly subjective task. Hence, there is no unanimously agreed standard for measuring aesthetic value. In spite of the lack of firm rules, certain features in photographic images are believed, by many, to please humans more than certain others. In this paper, we treat the challenge of automatically inferring aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated online photo sharing Website as data source. We extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images. Automated classifiers are built using support vector machines and classification trees. Linear regression on polynomial terms of the features is also applied to infer numerical aesthetics ratings. The work attempts to explore the relationship between emotions which pictures arouse in people, and their low-level content. Potential applications include content-based image retrieval and digital photography.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Airlines.Net, http://www.airliners.net
Arnheim, R.: Art and Visual Perception: A Psychology of the Creative Eye. University of California Press, Berkeley (1974)
ARTStor.org, http://www.artstor.org
Barnard, K., Duygulu, P., Forsyth, D., de. Freitas, N., Blei, D.M., Jordan, M.I.: Matching Words and Pictures. J. Machine Learning Research 3, 1107–1135 (2003)
Blum, A.L., Langley, P.: Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence 97(1-2), 245–271 (1997)
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Color and Texture-Based Image Segmentation using EM and its Application to Image Querying and Classification. IEEE Trans. on Pattern Analysis and Machine Intelli. 24(8), 1026–1038 (2002)
Chen, C.-c., Wactlar, H., Wang, J.Z., Kiernan, K.: Digital Imagery for Significant Cultural and Historical Materials - An Emerging Research Field Bridging People, Culture, and Technologies. Int’l J. on Digital Libraries 5(4), 275–286 (2005)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees, Wadsworth, Belmont, CA (1983)
Chang, C.-c., Lin, C.-j.: LIBSVM: A Library for SVM (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (1992)
Flickr, http://www.flickr.com
Li, J., Wang, J.Z.: Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach. IEEE Trans. on Pattern Analysis and Machine Intelli. 25(9), 1075–1088 (2003)
Ma, W.Y., Manjunath, B.S.: NeTra: A Toolbox for Navigating Large Image Databases. Multimedia Systems 7(3), 184–198 (1999)
Manjunath, B.S., Ma, W.Y.: Texture Features for Browsing and Retrieval of Image Data. IEEE Trans. on Pattern Analysis and Machine Intelli. 18(8), 837–842 (1996)
Photo.Net, http://www.photo.net
Photo.NetRatingSystem, http://photo.net/gallery/photocritique/standards
Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover’s Distance as a Metric for Image Retrieval. Int’l. J. Computer Vision 4(2), 99–121 (2000)
Smeulders, A.W., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-Based Image Retrieval at the End of the Early Years. IEEE Trans. on Pattern Analysis and Machine Intelli. 22(12), 1349–1380 (2000)
Therneau, T.M., Atkinson, E.J.: An Introduction to Recursive Partitioning Using RPART Routines. Technical Report, Mayo Foundation (1997)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries. IEEE Trans. on Pattern Analysis and Machine Intelli. 23(9), 947–963 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Datta, R., Joshi, D., Li, J., Wang, J.Z. (2006). Studying Aesthetics in Photographic Images Using a Computational Approach. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3953. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744078_23
Download citation
DOI: https://doi.org/10.1007/11744078_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33836-9
Online ISBN: 978-3-540-33837-6
eBook Packages: Computer ScienceComputer Science (R0)