Abstract
Many people fail to take exquisite pictures in a beautiful scenery for the lack of professional photography knowledge. In this paper, we focus on how to aid people to master daily life photography using a computational layout recommendation method. Given a selected scene, we first generate several synthetic photos with different layouts using 3D estimation. Then we employ a 3D layout aesthetic estimation model to rank the proposed photos. The results with high scores are selected as layout recommendations, which is then translated to a hint for where people shall locate. The key to our success lies on the combination of 3D structures with aesthetic models. The subjective evaluation shows superior preference of our method to previous work. We also give a few application examples to show the power of our method in creating better daily life photographs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bourke, S., McCarthy, K., Smyth, B.: The social camera: a case-study in contextual image recommendation. In: Proceedings of the 16th International Conference on Intelligent User Interfaces, pp. 13–22. ACM (2011)
Tian, Y., Wang, W., Gong, X., Que, X., Ma, J.: An enhanced personal photo recommendation system by fusing contextual and textual features on mobile device. IEEE Trans. Consum. Electron. 59(1), 220–228 (2013)
Elahi, N., Karlsen, R., Holsbø, E.J.: Personalized photo recommendation by leveraging user modeling on social network. In: Proceedings of International Conference on Information Integration and Web-Based Applications and Services, p. 68. ACM (2013)
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. 69(1), 3–29 (2014)
Xiang, Y., Savarese, S.: Estimating the aspect layout of object categories. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3410–3417. IEEE (2012)
Geiger, A., Wojek, C., Urtasun, R.: Joint 3D estimation of objects and scene layout. In: Advances in Neural Information Processing Systems, pp. 1467–1475 (2011)
Andriluka, M., Roth, S., Schiele, B.: Monocular 3D pose estimation and tracking by detection. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 623–630. IEEE (2010)
Kumar, S., Hebert, M.: Discriminative fields for modeling spatial dependencies in natural images. In: NIPS (2003)
Saxena, A., Chung, S.H., Ng, A.Y.: Learning depth from single monocular images. In: Advances in Neural Information Processing Systems, pp. 1161–1168 (2005)
Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 419–426. IEEE (2006)
Luo, W., Wang, X., Tang, X.: Content-based photo quality assessment. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2206–2213. IEEE (2011)
Marchesotti, L., Perronnin, F., Larlus, D., Csurka, G.: Assessing the aesthetic quality of photographs using generic image descriptors. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1784–1791. IEEE (2011)
Liu, L., Chen, R., Wolf, L., Cohen-Or, D.: Optimizing photo composition. In: Computer Graphics Forum, vol. 29, pp. 469–478. Wiley Online Library (2010)
Lo, K.Y., Liu, K.H., Chen, C.S.: Assessment of photo aesthetics with efficiency. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 2186–2189. IEEE (2012)
Gupta, S., Girshick, R., Arbeláez, P., Malik, J.: Learning rich features from RGB-D images for object detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 345–360. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10584-0_23
Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3485–3492. IEEE (2010)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Grill, T., Scanlon, M.: Photographic Composition. Amphoto Books, New York (1990)
Krages, B.: Photography: The Art of Composition. Skyhorse Publishing, Inc., New York (2012)
Ju, R., Ge, L., Geng, W., Ren, T., Wu, G.: Depth saliency based on anisotropic center-surround difference. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 1115–1119. IEEE (2014)
Cheng, M.M., Warrell, J., Lin, W.Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1536 (2013)
Acknowledgments
This work is supported by National Science Foundation of China (61321491, 61202320), Research Project of Excellent State Key Laboratory (61223003), Research Fund of the State Key Laboratory for Novel Software Technology at Nanjing University (ZZKT2016B09), and Collaborative Innovation Center of Novel Software Technology and Industrialization.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Zhang, B., Ju, R., Ren, T., Wu, G. (2016). Say Cheese: Personal Photography Layout Recommendation Using 3D Aesthetics Estimation. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-48896-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48895-0
Online ISBN: 978-3-319-48896-7
eBook Packages: Computer ScienceComputer Science (R0)