Abstract
We present a novel data-driven category-based approach to automatically assess the aesthetic appeal of photographs. In order to tackle this problem, a novel set of image segmentation methods based on feature contrast are introduced, such that luminance, sharpness, saliency, color chroma, and a measure of region relevance are computed to generate different image partitions. Image aesthetic features are computed on these regions (e.g. sharpness, colorfulness, and a novel set of light exposure features). In addition, color harmony, image simplicity, and a novel set of image composition features are measured on the overall image. Support Vector Regression models are generated for each of 7 popular image categories: animals, architecture, cityscape, floral, landscape, portraiture and seascapes. These models are analyzed to understand which features have greater influence in each of those categories, and how they perform with respect to a generic state of the art model.
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References
Benzaquen, S.: Postcolonial aesthetic experiences: thinking aesthetic categories in the face of catastrophe at the beginning of the twenty-first century. In: European Congress of Aesthetics (2010)
Bhattacharya, S., Sukthankar, R., Shah, M.: A framework for photo-quality assessment and enhancement based on visual aesthetics. In: Proc. of ACM Multimedia, pp. 271–280 (2010)
Cohen-Or, D., Sorkine, O., Gal, R., Leyvand, T., Xu, Y.-Q.: Color harmonization. ACM Transactions on Graphics 25(3), 624–630 (2006)
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, Part III. LNCS, vol. 3953, pp. 288–301. Springer, Heidelberg (2006)
Dyer, A.P.: A study of photographic chiaroscuro, M.A. dissertation. University of Northern Colorado (2005)
Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)
Freeman, M.: The image. revised edition. William Collins Sons & Co Ltd., (1990)
Gasparini, F., Schettini, R.: Color balancing of digital photos using simple image statistics. Pattern Recognition 37(6), 1201–1217 (2004)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: NIPS (2006)
Hasler, D., Susstrunk, S.: Measuring colourfulness in natural images. SPIE/IS&T Hum. Vis. Elec. Img. 5007, 87–95 (2003)
Kant, I.: The critique of judgement. Forgotten Books, forgottenbooks.org (2008)
Karatzoglou, A., Smola, A., Hornik, K., Zeileis, A.: Kernlab – an S4 package for kernel methods in R. Journal of Statistical Software 11(9), 1–20 (2004)
Li, C., et al.: Aesthetics quality assessment of consumer photos with faces. In: Proceedings of IEEE ICIP, pp. 3221–3224 (2010)
Luo, Y., Tang, X.: Photo and Video Quality Evaluation: Focusing on the Subject. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 386–399. Springer, Heidelberg (2008)
Meer, P., Georgescu, B.: Edge detection with embedded confidence. Transaction in Pattern Analysis and Machine Intelligence 12(23), 1351–1365 (2001)
Moorthy, A.K., Obrador, P., Oliver, N.: Towards Computational Models of the Visual Aesthetic Appeal of Consumer Videos. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 1–14. Springer, Heidelberg (2010)
Obrador, P., Anguera, X., de Oliveira, R., Oliver, N.: The role of tags and image aesthetics in social image search. In: Proc. of the SIGMM WSM, pp. 65–72 (2009)
Obrador, P., de Oliveira, R., Oliver, N.: Supporting personal photo storytelling for social albums. In: Proc. of ACM Multimedia, pp. 561–570 (2010)
Obrador, P., Moroney, N.: Low-level features for image appeal measurement. In: Proceedings of the SPIE, vol. 7242 (2009)
Obrador, P., Schmidt-Hackenberg, L., Oliver, N.: The role of image composition in image aesthetics. In: Proc. of IEEE ICIP, pp. 3185–3188 (2010)
Peli, E.: Contrast in complex images. Journal of the Optical Society of America 7(10), 2032–2040 (1990)
Rice, P.: Professional Techniques for Black & White Digital Photography. Amherst Media, Inc. (2005)
Wong, L.K., Low, K.L.: Saliency-enhanced image aesthetics class prediction. In: Proceedings of IEEE ICIP, pp. 997–1000 (2009)
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Obrador, P., Saad, M.A., Suryanarayan, P., Oliver, N. (2012). Towards Category-Based Aesthetic Models of Photographs. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, CW., Andreopoulos, Y., Breiteneder, C. (eds) Advances in Multimedia Modeling. MMM 2012. Lecture Notes in Computer Science, vol 7131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27355-1_9
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DOI: https://doi.org/10.1007/978-3-642-27355-1_9
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