Strategy for aesthetic photography recommendation via collaborative composition model

Yanhao Zhang; Qingming Huang; Lei Qin; Sicheng Zhao; Xiusheng Lu; Xiaoshuai Sun; Hongxun Yao

Strategy for aesthetic photography recommendation via collaborative composition model

View Fulltext

Author(s): Yanhao Zhang ¹ ; Qingming Huang ^{1, 2} ; Lei Qin ³ ; Sicheng Zhao ¹ ; Xiusheng Lu ¹ ; Xiaoshuai Sun ¹ ; Hongxun Yao ¹
- Affiliations: 1: School of Computer Science, Harbin Institute of Technology, Harbin 150001, People's Republic of China ;
  2: School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China ;
  3: Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
Source: Volume 9, Issue 5, October 2015, p. 691 – 698
DOI: 10.1049/iet-cvi.2014.0276 , Print ISSN 1751-9632, Online ISSN 1751-9640

Received 25/08/2014, Accepted 19/02/2015, Revised 22/01/2015, Published 02/06/2015

In this study, the authors propose a collaborative composition model for automatically recommending suitable positions and poses in the scene of photography taken by amateurs. By analysing aesthetic-aware features, the authors' strategy jointly takes attention and geometry composition into account to learn the aesthetic manifestation knowledge of professional photographers. Firstly, aesthetic composition representation exploits the strength of visual saliency to explicitly encode the spatial correlation of the professional photos. Secondly, ℓ2 regularised least square is adopted to constrain the representation coefficients, which provides a fast solution in selecting aesthetic candidates collaboratively. In addition, a novel confidence measure scheme is further designed based on reconstruction errors and the reference photos are updated adaptively according to the composition rules. Both qualitative and quantitative evaluations show that the model performs well for the portrait photographing recommendation.

References

1. 1)
  - 2. Li, C., Gallagher, A., Loui, A.C., Chen, T.: ‘Aesthetic quality assessment of consumer photos with faces’. IEEE ICIP, 2010, pp. 3221–3224.
2. 2)
  - 12. Tong, H., Li, M., Zhang, H.-J., He, J., Zhang, C.: ‘Classification of digital photos taken by photographers or home users’. PCM, Springer, 2005, pp. 198–205.
3. 3)
  - 16. Bhattacharya, S., Sukthankar, R., Shah, M.: ‘A framework for photoquality assessment and enhancement based on visual aesthetics’. Proc. of ACM MM, 2010, pp. 271–280.
4. 4)
  - 25. Zhou, W., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600–612 (doi: 10.1109/TIP.2003.819861).
5. 5)
  - 18. Yu, F.X., Ji, R., Chang, S.-F.: ‘Active query sensing for mobile location search’. Proc. of ACM MM, 2011, pp. 3–12.
6. 6)
  - 25. Zhang, Y., Qin, L., Yao, H., Xu, P., Huang, Q.: ‘Beyond particle flow: bag of trajectory graphs for dense crowd event recognition’. IEEE ICIP, 2013.
7. 7)
  - 1. Cheng, B., Ni, B., Yan, S., Tian, Q.: ‘Learning to photograph’. Proc. of ACM MM, 2010, pp. 291–300.
8. 8)
  - 23. Jiang, F., Zhang, S., Wu, S., Gao, Y., Zhao, D.: ‘Multi-layered gesture recognition with kinect’, J. Mach. Learn. Res., 2014.
9. 9)
  - 15. Sun, X., Yao, H., Ji, R., Liu, S.: ‘Photo assessment based on computational visual attention model’. Proc. of ACM MM, 2009, pp. 541–544.
10. 10)
  - 13. Datta, R., Joshi, D., Li, J., Wang, J.Z.: ‘Studying aesthetics in photographic images using a computational approach’. ECCV, Springer, 2006, pp. 288–301.
11. 11)
  - 9. Tong, H., Li, M., Zhang, H.-J., Zhang, C., He, J., Ma, W.-Y.: ‘Learning no-reference quality metric by examples’. IEEE MMM, 2005, pp. 247–254.
12. 12)
  - 20. Guo, C., Ma, Q., Zhang, L.: ‘Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform’. IEEE CVPR, 2008, pp. 1–8.
13. 13)
  - 19. Ferrari, V., Marin-Jimenez, M., Zisserman, A.: ‘Pose search: retrieving people using their pose’. IEEE CVPR, 2009, pp. 1–8.
14. 14)
  - 27. Xiao, Z., Lu, H., Wang, D.: ‘Object tracking with l2-rls’. IEEE ICPR, 2012, pp. 1351–1354.
15. 15)
  - 22. Zhang, S., Yao, H., Sun, X., et al: ‘Action recognition based on overcomplete independent components analysis’, Inf. Sci., 2014.
16. 16)
  - 26. Zhang, Y., Qin, L., Ji, R., Yao, H., Huang, Q.: ‘Social attribute aware force model: exploiting richness of interaction for abnormal crowd detection’, IEEE Trans. Circuits Syst. Video Technol., 2014.
17. 17)
  - 8. Zhang, S., Yao, H., Sun, X., Lu, X.: ‘Sparse coding based visual tracking: Review and experimental comparison’, Pattern Recognit., 2013, 46, (7), pp. 1772–1788 (doi: 10.1016/j.patcog.2012.10.006).
18. 18)
  - 10. Freeman, M.: ‘The complete guide to light and lighting in digital photography’ (Sterling Publishing Company, Inc., 2007).
19. 19)
  - 28. Zhang, Y., Sun, X., Yao, H., Qin, L., Huang, Q.: ‘Aesthetic composition representation for portrait photographing recommendation’. IEEE ICIP, 2012, pp. 2753–2756.
20. 20)
  - 11. Ke, Y., Tang, X., Jing, F.: ‘The design of high-level features for photo quality assessment’. IEEE CVPR, 2006, vol. 1, pp. 419–426.
21. 21)
  - 3. Obrador, P., Schmidt-Hackenberg, L., Oliver, N.: ‘The role of image composition in image aesthetics’. IEEE ICIP, 2010, pp. 3185–3188.
22. 22)
  - 8. Sheikh, H.R., Bovik, A.C., Cormack, L.: ‘No-reference quality assessment using natural scene statistics: Jpeg2000’, IEEE Trans. Image Process., 2005, 14, (11), pp. 1918–1927 (doi: 10.1109/TIP.2005.854492).
23. 23)
  - 6. Damera-Venkata, N., Kite, T.D., Geisler, W.S., Evans, B.L., Bovik, A.C.: ‘Image quality assessment based on a degradation model’, IEEE Trans. Image Process., 2000, 9, (4), pp. 636–650 (doi: 10.1109/83.841940).
24. 24)
  - 14. Luo, Y., Tang, X.: ‘Photo and video quality evaluation: focusing on the subject’. ECCV, Springer, 2008, pp. 386–399.
25. 25)
  - 5. Li, C., Loui, A.C., Chen, T.: ‘Towards aesthetics: a photo quality assessment and photo selection system’. Proc. of ACM MM, 2010, pp. 827–830.
26. 26)
  - 4. Eom, W., Lee, S., De Neve, W., Ro, Y.M.: ‘Improving image tag recommendation using favorite image context’. IEEE ICIP, 2011, pp. 2445–2448.
27. 27)
  - 17. Xu, P., Yao, H., Ji, R., Liu, X.-M., Sun, X.: ‘Where should i stand? Learning based human position recommendation for mobile photographing’, Multimedia Tools Appl., 2014, 69, (1), pp. 3–29 (doi: 10.1007/s11042-012-1343-2).
28. 28)
  - 21. Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: ‘Salient region detection and segmentation’. Computer Vision Systems, Springer, 2008, pp. 66–75.

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Strategy for aesthetic photography recommendation via collaborative composition model

References

Related content