Skip to main content

Advertisement

Springer Nature Link
Log in

Where should I stand? Learning based human position recommendation for mobile photographing

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, we study the problem of human position recommendation in mobile photographing and propose a learning-based method to summarize the photographing knowledge from massive social images to improve the robustness and effectiveness. In contrast to existing photographing guide methods, we focus on turning to the collaborative web data source and learning the distribution of human position. To overcome the challenges in landmark image alignment and the relative human position projection, we propose a 3D reconstruction-based method to align the background region and human region into a uniform coordinate system. Finally, a camera-view sensitive human position recommendation strategy is carried out. A dataset containing 30,000 photos of ten landmark scenes is collected from Flickr, and a group of experiments are conducted comparing both our alternatives and various other baseline methods. Moreover, an application is developed on mobile phones to implement the real-time photographing recommendation. The experimental results show that our proposed framework achieves promising results, which demonstrate the robustness and effectiveness of our approach.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Agarwal S, Snavely N, Simon I, Seitz S, Szeliski R (2009) Building rome in a day. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 72–79

  2. Arya S, Mount D (1993) Approximate nearest neighbor queries in fixed dimensions. In: Proceedings of the fourth annual ACM-SIAM symposium on discrete algorithms. Society for industrial and applied mathematics, pp 271–280

  3. Bhattacharya S, Sukthankar R, Shah M (2010) A framework for photo-quality assessment and enhancement based on visual aesthetics. In: Proceedings of the international conference on Multimedia. ACM, pp 271–280

  4. Chen D, Baatz G, Koser K, Tsai S, Vedantham R, Pylvanainen T, Roimela K, Chen X, Bach J, Pollefeys M et al (2011) City-scale landmark identification on mobile devices. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 737–744

  5. Chen H, Liu BB, Luo H, Lu ZM (2012) Fast image artistic style learning using twin-codebook vector quantization. J Inf Hiding Multimedia Signal Process 3(1):66–70

    Google Scholar 

  6. Cheng B, Ni B, Yan S, Tian Q (2010) Learning to photograph. In: Proceedings of the international conference on multimedia. ACM, pp 291–300

  7. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1. IEEE, pp 886–893

  8. Damera-Venkata N, Kite T, Geisler W, Evans B, Bovik A (2000) Image quality assessment based on a degradation model. IEEE Trans Image Process 9(4):636–650

    Article  Google Scholar 

  9. Datta R, Joshi D, Li J, Wang J (2006) Studying aesthetics in photographic images using a computational approach. In: Computer vision–ECCV 2006, pp 288–301

  10. Fischler M, Bolles R (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  11. Freeman M (2006) The complete guide to light & lighting in digital photography. Lark Books (NC)

  12. Freeman M (2007) The photographer’s eye: composition and design for better digital photos. Focal Pr

  13. Girod B, Chandrasekhar V, Chen D, Cheung N, Grzeszczuk R, Reznik Y, Takacs G, Tsai S, Vedantham R (2011) Mobile visual search. IEEE Signal Process Mag 28(4):61–76

    Article  Google Scholar 

  14. Hartley R, Zisserman A, Ebrary I (2003) Multiple view geometry in computer vision, vol 2. Cambridge Univ Press

  15. Hays J, Efros AA (2008) im2gps: estimating geographic information from a single image. In: Proceedings of the IEEE conf. on computer vision and pattern recognition (CVPR)

  16. Ji R, Duan L, Chen J, Yao H, Rui Y, Chang S, Gao W (2011) Towards low bit rate mobile visual search with multiple-channel coding. In: Proceedings of the 19th ACM international conference on multimedia. ACM, pp 573–582

  17. Ji R, Duan L-Y, Chen J, Yao H, Yuan J, Rui Y, Gao W (2012) Location discriminative vocabulary coding for mobile landmark search. Int J Comput Vis 96(3):290–314

    Article  MATH  Google Scholar 

  18. Joshi D, Gallagher A, Yu J, Luo J (2010) Inferring photographic location using geotagged web images. Multimed Tools Appl 56(1):131–153

    Article  Google Scholar 

  19. Ke Y, Tang X, Jing F (2006) The design of high-level features for photo quality assessment. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 1. IEEE, pp 419–426

  20. Kretzschmar H, Stachniss C, Plagemann C, Burgard W (2008) Estimating landmark locations from geo-referenced photographs. In: IEEE/RSJ international conference on intelligent robots and systems, 2008. IROS 2008. IEEE, pp 2902–2907

  21. Li X (2002) Blind image quality assessment. In: Proceedings on 2002 international conference on image processing, 2002, vol 1. IEEE, pp 449–452

  22. Li X, Wu C, Zach C, Lazebnik S, Frahm J (2008) Modeling and recognition of landmark image collections using iconic scene graphs. In: Proc. ECCV, vol 8

  23. Li Y, Crandall D, Huttenlocher D (2009) Landmark classification in large-scale image collections. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 1957–1964

  24. Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  25. Luo Y, Tang X (2008) Photo and video quality evaluation: focusing on the subject. In: Proceedings of the 10th European conference on computer vision: part III. Springer, pp 386–399

  26. Robertson D, Cipolla R (2004) An image-based system for urban navigation. In: The 15th British machine vision conference (BMVC), pp 819–828

  27. Sattler T, Leibe B, Kobbelt L (2011) Fast image-based localization using direct 2d-to-3d matching. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 667–674

  28. Sheikh H, Bovik A, Cormack L (2005) No-reference quality assessment using natural scene statistics: Jpeg2000. IEEE Trans Image Process 14(11):1918–1927

    Article  Google Scholar 

  29. Snavely N, Seitz S, Szeliski R (2006) Photo tourism: exploring photo collections in 3d. In: ACM transactions on graphics (TOG), vol 25. ACM, pp 835–846

  30. Snavely N, Seitz S, Szeliski R (2008) Modeling the world from internet photo collections. Int J Comput Vis 80(2):189–210

    Article  Google Scholar 

  31. Sun X, Yao H, Ji R, Liu S (2009) Photo assessment based on computational visual attention model. In: ACM MM, vol 1658

  32. Tong H, Li M, Zhang H, He J, Zhang C (2005) Classification of digital photos taken by photographers or home users. In: Advances in multimedia information processing-PCM 2004, pp 198–205

  33. Tong H, Li M, Zhang H, Zhang C, He J, Ma W (2005) Learning no-reference quality metric by examples. In: Proceedings of the 11th international multimedia modelling conference, 2005. MMM 2005. IEEE, pp 247–254

  34. Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  35. Wu C, Agarwal S, Curless B, Seitz S (2011) Multicore bundle adjustment. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3057–3064

  36. Yeh T, Tollmar K, Darrell T (2004) Searching the web with mobile images for location recognition. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004, vol 2. IEEE, pp 76–81

  37. Yu F, Ji R, Chang S (2011) Active query sensing for mobile location search. In: Proceedings of the 19th ACM international conference on multimedia. ACM, pp 3–12

  38. Zhang W, Kosecka J (2006) Image based localization in urban environments. In: Third international symposium on 3D data processing, visualization, and transmission. IEEE, pp 33–40

Download references

Acknowledgements

This work was supported in part by the National Science Foundation of China (No. 61071180 & No. 61133003).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Harbin, 150001, China

    Pengfei Xu, Hongxun Yao, Rongrong Ji, Xian-Ming Liu & Xiaoshuai Sun

Authors
  1. Pengfei Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hongxun Yao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Rongrong Ji
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xian-Ming Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xiaoshuai Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Hongxun Yao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, P., Yao, H., Ji, R. et al. Where should I stand? Learning based human position recommendation for mobile photographing. Multimed Tools Appl 69, 3–29 (2014). https://doi.org/10.1007/s11042-012-1343-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1343-2

Keywords