Studying aesthetics in photographic images using a computational approach

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Reference

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)

DOI

http://link.springer.com/chapter/10.1007%2F11744078_23

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.

Extended Abstract

Bibtex

Used 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) http://dx.doi.org/10.1162/153244303322533214

Blum, A.L., Langley, P.: Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence 97(1-2), 245–271 (1997) http://dx.doi.org/10.1016/S0004-3702(97)00063-5

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) http://dx.doi.org/10.1109/TPAMI.2002.1023800

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) http://dx.doi.org/10.1007/s00799-004-0097-5

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) http://dx.doi.org/10.1109/TPAMI.2003.1227984

Ma, W.Y., Manjunath, B.S.: NeTra: A Toolbox for Navigating Large Image Databases. Multimedia Systems 7(3), 184–198 (1999) http://dx.doi.org/10.1007/s005300050121

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) http://dx.doi.org/10.1109/34.531803

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) http://dx.doi.org/10.1023/A:1026543900054

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) http://dx.doi.org/10.1109/34.895972

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) http://dx.doi.org/10.1109/34.955109


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