Skip to main content

WHAT2PRINT: Learning Image Evaluation

  • Conference paper
  • First Online:
  • 1832 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  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. 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. 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. 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. 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. 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. Liu, L., Chen, R., Wolf, L., Cohen-Or, D.: Optimizing photo composition. Comput. Graph. Forum (Proc. Eurograph.) 29, 469–478 (2010)

    Article  Google Scholar 

  8. Krages, B.: Photography: The Art of Composition. Allworth Press, New York (2005)

    Google Scholar 

  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. 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

    Article  Google Scholar 

  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. Barnbaum, B.: The Art of Photography: An Approach to Personal Expression. Rocky Nook, Santa Barbara (2010)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  14. Manav, B.: Color-emotion associations and color preferences: a case study for residences. Color Res. Appl. 32, 144–150 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  16. Tang, X., Luo, W., Wang, X.: Content-based photo quality assessment. IEEE Trans. Multimed. 15, 1930–1943 (2013)

    Article  Google Scholar 

  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. 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. 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. Barnbaum, B.: The Essence of Photography: Seeing and Creativity. Rocky Nook, Santa Barbara (2014)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–697 (1986)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  27. Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Clark F. Olson .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27863-6_55

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics