Abstract
The popularity of digital photography has changed the way images that are taken, processed, and stored. This has created a demand for systems that can evaluate the aesthetic quality of images. Applications that auto-assess image aesthetic quality and modify images to raise their aesthetic quality are widely available, but applications that automatically select aesthetic images from a given image collection are limited. The goal of this project is to create a portable application that can recommend user-given images from a given image collection, using criteria learned from user preferences. We train a Support Vector Machine on seven extracted image features. This system achieves a correct prediction rate of 70 % on a public image dataset. The use of additional or improved features should yield increased prediction rates.
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References
Datta, R., Li, J., Wang, J.Z.: Algorithmic inferencing of aesthetics and emotion in natural images: an exposition. In: Proceedings of the IEEE International Conference on Image Processing, pp. 105–108 (2008)
Murray, N., Marchesotti, L., Perronnin, F.: AVA: a large-scale database for aesthetic visual analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2408–2415 (2012)
Su, H.H., Chen, T.W., Kao, C.C., Hsu, W.H., Chien, S.Y.: Scenic photo quality assessment with bag of aesthetics-preserving features. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 1213–1216 (2011)
Datta, R., Wang, J.Z.: Acquine: aesthetic quality inference engine real-time automatic rating of photo aesthetics. In: Proceedings of the ACM International Conference on Multimedia Information Retrieval, pp. 421–424 (2010)
Lo, L.Y., Chen, J.C.: A statistic approach for photo quality assessment. In: Proceedings of the International Conference on Information Security and Intelligence Control (ISIC), pp. 107–110 (2012)
Li, C., Loui, A.C., Chen, T.: Towards aesthetics: a photo quality assessment and photo selection system. In: Proceedings of the International Conference on Multimedia, pp. 827–830 (2010)
Liu, L., Chen, R., Wolf, L., Cohen-Or, D.: Optimizing photo composition. Comput. Graph. Forum (Proc. Eurograph.) 29, 469–478 (2010)
Krages, B.: Photography: The Art of Composition. Allworth Press, New York (2005)
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, pp. 271–280 (2010)
Yeh, C.H., Barsky, B.A., Ouhyoung, M.: Personalized photograph ranking and selection system considering positive and negative user feedback. ACM Trans. Multimed. Comput. Commun. Appl. 10, 1–20 (2014). Article No. 36
Ryu, D.S., Kim, K.H., Park, S.Y., Cho, H.G.: A web-based photo management system for large photo collections with user-customizable quality assessment. In: Proceedings of the ACM Symposium on Applied Computing, pp. 1229–1236 (2011)
Barnbaum, B.: The Art of Photography: An Approach to Personal Expression. Rocky Nook, Santa Barbara (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)
Manav, B.: Color-emotion associations and color preferences: a case study for residences. Color Res. Appl. 32, 144–150 (2007)
Gao, X.P., Xin, J.H., Sato, T., Hansuebsai, A., Scalzo, M., Kajiwara, K., Guan, S.S., Valldeperas, J., Lis, M.J., Billger, M.: Analysis of cross-cultural color emotion. Color Res. Appl. 32, 223–229 (2007)
Tang, X., Luo, W., Wang, X.: Content-based photo quality assessment. IEEE Trans. Multimed. 15, 1930–1943 (2013)
Nishiyama, M., Okabe, T., Sato, I., Sato, Y.: Aesthetic quality classification of photographs based on color harmony. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 33–40 (2011)
Ke, Y., Tang, X.: The design of high-level features for photo quality assessment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 419–426 (2006)
Lo, K.Y., Liu, K.H., Chen, C.S.: Assessment of photo aesthetics with efficiency. In: Proceedings of the IAPR International Conference on Pattern Recognition, pp. 2186–2189 (2012)
Barnbaum, B.: The Essence of Photography: Seeing and Creativity. Rocky Nook, Santa Barbara (2014)
Su, H.H., Chen, T.W., Kao, C.C., Hsu, W.H.: Preference-aware view recommendation system for scenic photos based on bag-of-aesthetics-preserving features. IEEE Trans. Multimed. 14, 833–843 (2012)
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)
Datta, R., Li, J., Wang, J.Z.: Learning the consensus on visual quality for next-generatoin image management. In: Proceedings of the 15th International Conference on Multimedia, pp. 533–536 (2007)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–697 (1986)
Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
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She, B., Olson, C.F. (2015). WHAT2PRINT: Learning Image Evaluation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_55
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DOI: https://doi.org/10.1007/978-3-319-27863-6_55
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