Abstract
People often take photographs at tourist sites and these pictures usually have two main elements: a person in the foreground and scenery in the background. This type of “souvenir photo” is one of the most common photos clicked by tourists. Although algorithms that aid a user-photographer in taking a well-composed picture of a scene exist [Ni et al. 2013], few studies have addressed the issue of properly positioning human subjects in photographs. In photography, the common guidelines of composing portrait images exist. However, these rules usually do not consider the background scene. Therefore, in this article, we investigate human-scenery positional relationships and construct a photographic assistance system to optimize the position of human subjects in a given background scene, thereby assisting the user in capturing high-quality souvenir photos. We collect thousands of well-composed portrait photographs to learn human-scenery aesthetic composition rules. In addition, we define a set of negative rules to exclude undesirable compositions. Recommendation results are achieved by combining the first learned positive rule with our proposed negative rules. We implement the proposed system on an Android platform in a smartphone. The system demonstrates its efficacy by producing well-composed souvenir photos.
- Radhakrishna Achanta, Francisco Estrada, Patricia Wils, and Sabine Süsstrunk. 2008. Salient region detection and segmentation. In Computer Vision Systems. Springer, 66--75. Google ScholarDigital Library
- Jongmin Baek, Dawid Pajak, Kihwan Kim, Kari Pulli, and Marc Levoy. 2013. WYSIWYG computational photography via viewfinder editing. ACM Transactions on Graphics 32, 6 (2013), 198. Google ScholarDigital Library
- Dana H Ballard. 1981. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13, 2 (1981), 111--122.Google ScholarCross Ref
- Subhabrata Bhattacharya, Rahul Sukthankar, and Mubarak Shah. 2010. A framework for photo-quality assessment and enhancement based on visual aesthetics. In Proceedings of the International Conference on Multimedia. ACM, 271--280. Google ScholarDigital Library
- Dmitri Bitouk, Neeraj Kumar, Samreen Dhillon, Peter Belhumeur, and Shree K. Nayar. 2008. Face swapping: Automatically replacing faces in photographs. ACM Transactions on Graphics 27, 3 (2008), 39. Google ScholarDigital Library
- Charles A. Bouman, Michael Shapiro, G. W. Cook, C. Brian Atkins, and Hui Cheng. 1997. Cluster: An Unsupervised Algorithm for Modeling Gaussian Mixtures. https://engineering.purdue.edu/∼bouman/software/cluster/. (1997).Google Scholar
- Tao Chen, Ming-Ming Cheng, Ping Tan, Ariel Shamir, and Shi-Min Hu. 2009. Sketch2Photo: Internet image montage. ACM Transactions on Graphics 28, 5 (2009), 124. Google ScholarDigital Library
- Ming-Ming Cheng, Guo-Xin Zhang, Niloy J. Mitra, Xiaolei Huang, and Shi-Min Hu. 2011. Global contrast based salient region detection. In Proceedings of Computer Vision and Pattern Recognition. IEEE, 409--416. Google ScholarDigital Library
- Franklin C. Crow. 1984. Summed-area tables for texture mapping. In Proceedings of ACM SIGGRAPH, Vol. 18. ACM, 207--212. Google ScholarDigital Library
- Arthur P. Dempster, Nan M. Laird, and Donald B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (1977), 1--38.Google Scholar
- Zeev Farbman, Gil Hoffer, Yaron Lipman, Daniel Cohen-Or, and Dani Lischinski. 2009. Coordinates for instant image cloning. ACM Transactions on Graphics 28, 3 (2009), 67. Google ScholarDigital Library
- Douglas Hoffman. 2013. Portrait Photography Tip—Avoid Putting Peoples Head in the Horizon Line. http://mauiphototours.net/?p=389. (2013).Google Scholar
- Jiaya Jia, Jian Sun, Chi-Keung Tang, and Heung-Yeung Shum. 2006. Drag-and-drop pasting. ACM Transactions on Graphics 25, 3 (2006), 631--637. Google ScholarDigital Library
- Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu. 2002. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 7 (2002), 881--892. Google ScholarDigital Library
- Yan Ke, Xiaoou Tang, and Feng Jing. 2006. The design of high-level features for photo quality assessment. In Proceedings of Computer Vision and Pattern Recognition, Vol. 1. IEEE, 419--426. Google ScholarDigital Library
- Bert Krages. 2005. Photography: The art of composition. (1st ed.). Allworth Press.Google Scholar
- Daniel Kuettel and Vittorio Ferrari. 2012. Figure-ground segmentation by transferring window masks. In Proc. of Computer Vision and Pattern Recognition. IEEE, 558--565. Google ScholarDigital Library
- Ivan O. Kyrgyzov, Olexiy O. Kyrgyzov, Henri Maître, and Marine Campedel. 2007. Kernel MDL to determine the number of clusters. In Machine Learning and Data Mining in Pattern Recognition. Springer, 203--217. Google ScholarDigital Library
- Ligang Liu, Renjie Chen, Lior Wolf, and Daniel Cohen-Or. 2010a. Optimizing photo composition. Computer Graphics Forum 29, 2 (2010), 469--478.Google ScholarCross Ref
- Ligang Liu, Yong Jin, and Qingbiao Wu. 2010b. Realtime aesthetic image retargeting. In Proceedings of the International Conference on Computational Aesthetics in Graphics, Visualization and Imaging. Eurographics Association, 1--8. Google ScholarDigital Library
- Wei Luo, Xiaogang Wang, and Xiaoou Tang. 2011. Content-based photo quality assessment. In Proceedings of the International Conference on Computer Vision. IEEE, 2206--2213. Google ScholarDigital Library
- Yiwen Luo and Xiaoou Tang. 2008. Photo and video quality evaluation: Focusing on the subject. In Proceedings of the European Conference on Computer Vision. Springer, 386--399. Google ScholarDigital Library
- Bingbing Ni, Mengdi Xu, Bin Cheng, Meng Wang, Shuicheng Yan Yan, and Qi Tian. 2013. Learning to photograph: A compositional perspective. IEEE Transactions on Multimedia 15, 5 (2013), 1138--1151. Google ScholarDigital Library
- Masashi Nishiyama, Takahiro Okabe, Yoichi Sato, and Imari Sato. 2009. Sensation-based photo cropping. In Proceedings of the 17th ACM International Conference on Multimedia. ACM, 669--672. Google ScholarDigital Library
- Patrick Pérez, Michel Gangnet, and Andrew Blake. 2003. Poisson image editing. ACM Transactions on Graphics 22, 3 (2003), 313--318. Google ScholarDigital Library
- Carsten Rother. 2002. A new approach to vanishing point detection in architectural environments. Image and Vision Computing 20, 9 (2002), 647--655.Google ScholarCross Ref
- Michael W. Tao, Micah K. Johnson, and Sylvain Paris. 2010. Error-tolerant image compositing. In Proceedings of the European Conference on Computer Vision. Springer, 31--44. Google ScholarDigital Library
- Paul Viola and Michael Jones. 2001. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1. I-511--I-518.Google ScholarCross Ref
- Jianzhou Yan, Stephen Lin, Sing Bing Kang, and Xiaoou Tang. 2013. Learning the change for automatic image cropping. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 971--978. Google ScholarDigital Library
- Luming Zhang, Mingli Song, Qi Zhao, Xiao Liu, Jiajun Bu, and Chun Chen. 2013. Probabilistic graphlet transfer for photo cropping. IEEE Transactions on Image Processing 22, 2 (2013), 802--815. Google ScholarDigital Library
- Yanhao Zhang, Xiaoshuai Sun, Hongxun Yao, Lei Qin, and Qingming Huang. 2012. Aesthetic composition representation for portrait photographing recommendation. In Proceedings of the International Conference on Image Processing. IEEE, 2753--2756.Google Scholar
- David Ziser. 2010. Captured by the Light: The Essential Guide to Creating Extraordinary Wedding Photography. Pearson Education.Google Scholar
- Monte Zucker. 2007. Monte Zucker’s Portrait Photography Handbook. Amherst Media, Inc.Google Scholar
Index Terms
- Where2Stand: A Human Position Recommendation System for Souvenir Photography
Recommendations
Intelligent Portrait Composition Assistance: Integrating Deep-learned Models and Photography Idea Retrieval
Thematic Workshops '17: Proceedings of the on Thematic Workshops of ACM Multimedia 2017Retrieving photography ideas corresponding to a given location facilitates the usage of smart cameras, where there is a high interest among amateurs and enthusiasts to take astonishing photos at anytime and in any location. Existing research captures ...
Generating virtual camera compositions
IUI '01: Proceedings of the 6th international conference on Intelligent user interfacesThis paper describes work in progress to automatically generate camera shots featuring the composition techniques of expert photographers. This effort builds upon an automated camera planner that computes a shot satisfying a given set of constraints. In ...
Comments