Advertisement

WHAT2PRINT: Learning Image Evaluation

  • Bohao She
  • Clark F. OlsonEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)

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.

Keywords

Support Vector Machine Color Histogram Prefer Image Image Collection Golden Ratio 
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

  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    Liu, L., Chen, R., Wolf, L., Cohen-Or, D.: Optimizing photo composition. Comput. Graph. Forum (Proc. Eurograph.) 29, 469–478 (2010)CrossRefGoogle Scholar
  8. 8.
    Krages, B.: Photography: The Art of Composition. Allworth Press, New York (2005)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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. 36CrossRefGoogle Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    Barnbaum, B.: The Art of Photography: An Approach to Personal Expression. Rocky Nook, Santa Barbara (2010)Google Scholar
  13. 13.
    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) CrossRefGoogle Scholar
  14. 14.
    Manav, B.: Color-emotion associations and color preferences: a case study for residences. Color Res. Appl. 32, 144–150 (2007)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Tang, X., Luo, W., Wang, X.: Content-based photo quality assessment. IEEE Trans. Multimed. 15, 1930–1943 (2013)CrossRefGoogle Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    Barnbaum, B.: The Essence of Photography: Seeing and Creativity. Rocky Nook, Santa Barbara (2014)Google Scholar
  21. 21.
    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)CrossRefGoogle Scholar
  22. 22.
    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) CrossRefGoogle Scholar
  23. 23.
    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)Google Scholar
  24. 24.
    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)Google Scholar
  25. 25.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–697 (1986)CrossRefGoogle Scholar
  26. 26.
    Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)zbMATHCrossRefGoogle Scholar
  27. 27.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of Washington BothellBothellUSA

Personalised recommendations