Abstract
The John Ruskin’s 19th century adage suggests that personal taste is not merely an absolute set of aesthetic principles valid for everyone: actually, it is a process of interpretation which have also roots in one’s life experiences. This aspect represents nowadays a major problem for inferring automatically the quality of a picture. In this paper, instead of trying to solve this age-old problem, we consider an intriguing, orthogonal direction, aimed at discovering how different are the personal tastes. Given a set of preferred images of a user, obtained from Flickr, we extract a pool of low- and high-level features; LASSO regression is then exploited to learn the most discriminative ones, considering a group of 200 random Flickr users. Such aspects can be easily recovered, allowing to understand what is the “what we like” which distinguish us from the others. We then perform multi-class classification, where a test sample is a set of preferred pictures of an unknown user, and the classes are all the users. The results are surprising: given only 1 image as test, we can match the user preferences definitely more than the chance, and with 20 images we reach an nAUC of 91%, considering the cumulative matching characteristic curve. Extensive experiments promote our approach, suggesting new intriguing perspectives in the study of computational aesthetics.
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References
Leder, H., Belke, B., Oeberst, A., Augustin, D.: A model of aesthetic appreciation and aesthetic judgments. British Journal of Psychology 95, 489–508 (2004)
Martindale, C., Moore, K., Borkum, J.: Aesthetic preference: Anomalous findings for berlyne’s psychobiological theory. American Journal of Psychology 103, 53–80 (1990)
Bronstad, P., Russell, R.: Beauty is in the “we” of the beholder: greater agreement on facial attractiveness among close relations. Perception 36, 1674–1681 (2007)
Kaplan, R., Kaplan, S.: The Experience of Nature: A Psychological Perspective. Cambridge University Press (1989)
Bhattacharya, S., Sukthankar, R., Shah, M.: A framework for photo-quality assessment and enhancement based on visual aesthetics. In: Proceedings of the International Conference on Multimedia, MM 2010, pp. 271–280. ACM, New York (2010)
Adams, B.: Where does computational media aesthetics fit? IEEE Multimedia 10, 18–27 (2003)
Yeh, C.H., Ho, Y.C., Barsky, B.A., Ouhyoung, M.: Personalized photograph ranking and selection system. In: Proceedings of the International Conference on Multimedia, MM 2010, pp. 211–220. ACM, New York (2010)
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)
Su, H.H., Chen, T.W., Kao, C.C., Hsu, W.H., Chien, S.Y.: Preference-aware view recommendation system for scenic photos based on bag-of-aesthetics-preserving features. IEEE Transactions on Multimedia 14, 833–843 (2012)
Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: CVPR 2006, pp. 419–426. IEEE Computer Society, Washington, DC (2006)
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)
Biederman, I., Vessel, E.: Perceptual pleasure and the brain. American Scientist 94, 1–8 (2006)
Bozzon, A., Brambilla, M., Ceri, S.: Answering search queries with crowdsearcher. In: WWW, pp. 1009–1018 (2012)
Hielscher, R., Schaeben, H.: A novel pole figure inversion method: specification of the mtex algorithm. Journal of Applied Crystallography 41, 1024–1037 (2008)
Bachmann, F., Hielscher, R., Schaeben, H.: Texture analysis with mtex–free and open source software toolbox. Solid State Phenomena 160, 63–68 (2010)
Isola, P., Jianxiong, X., Torralba, A., Oliva, A.: What makes an image memorable? In: 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 145–152 (2011)
Curran, W., Moore, T., Kulesza, T., Wong, W., Todorovic, S., Stumpf, S., White, R., Burnett, M.M.: Towards recognizing ”cool”: can end users help computer vision recognize subjective attributes of objects in images? In: ACM International Conference on Intelligent User Interfaces, pp. 285–288 (2012)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)
Georgescu, C.: Synergism in low level vision. In: International Conference on Pattern Recognition, pp. 150–155 (2002)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D.: Discriminatively trained deformable part models, release 4 (2010), http://www.cs.brown.edu/~pff/latent-release4/
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 1627–1645 (2010)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)
Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. J. Comput. Vision 42, 145–175 (2001)
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 58, 267–288 (1994)
Moon, H., Phillips, P.: Computational and performance aspects of pca-based face-recognition algorithms. Perception 30, 303–321 (2001)
Cheng, D., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: Proceedings of British Machine Vision Conference (2011)
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Lovato, P. et al. (2013). Tell Me What You Like and I’ll Tell You What You Are: Discriminating Visual Preferences on Flickr Data. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_4
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DOI: https://doi.org/10.1007/978-3-642-37331-2_4
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