Abstract
In this paper, we construct a robot photographic system to search for the optimal viewpoint of a scene. Based on some known composition rules in the field of photography, we propose a novel aesthetic composition evaluation method by the use of Kullback-Leilber divergence. For viewpoint selection, we put forward a method called Composition-map, which can estimate the aesthetic value of scenes for each candidate viewpoint around the target group. At last, the effectiveness of our robot photographic system is confirmed with practical experiments.
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
Similar content being viewed by others
References
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)
Wu, Y., Bauckhage, C., Thurau, C.: The good, the bad, and the ugly: predicting aesthetic image labels. In: 20th IEEE International Conference on Pattern Recognition (ICPR), pp. 1586–1589 (2010)
Dunstan, B.: Composing Your Paintings. Watson-Guptill Publications, New York (1971)
Liu, L., Chen, R., Wolf, L., Cohen-Or, D.: Optimizing photo composition. Comput. Graph. Forum 29, 469–478 (2010)
Zhang, F., Wang, M., Hu, S.: Aesthetic image enhancement by dependence-aware object recomposition. IEEE Trans. Multimedia 15, 1480–1490 (2013)
Lan, K., Sekiyama, K.: Autonomous viewpoint selection of robots based on aesthetic composition evaluation of a photo. In: The 2015 IEEE Symposium Series on Computational Intelligence, pp. 295–300 (2015)
Byers, Z., Dixon, M., Goodier, K., Grimm, C., Smart, W.D.: An autonomous robot photographer. In: The 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2636–2641 (2003)
Byers, Z., Dixon, M., Smart, W.D., Grimm, C.: Say cheese! Experiences with a robot photographer. AI Mag. 25, 37–46 (2004)
Filliat, D., et al.: RGBD object recognition and visual texture classification for indoor semantic mapping. In: The 2012 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), pp. 127–132 (2012)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Lan, K., Sekiyama, K. (2016). Optimal Viewpoint Selection Based on Aesthetic Composition Evaluation Using Kullback-Leibler Divergence. In: Kubota, N., Kiguchi, K., Liu, H., Obo, T. (eds) Intelligent Robotics and Applications. ICIRA 2016. Lecture Notes in Computer Science(), vol 9834. Springer, Cham. https://doi.org/10.1007/978-3-319-43506-0_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-43506-0_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43505-3
Online ISBN: 978-3-319-43506-0
eBook Packages: Computer ScienceComputer Science (R0)