Autonomous Viewpoint Selection of Robot Based on Aesthetic Evaluation of a Scene

Open access


In this paper, we propose an optimal viewpoint selection system for monitoring robots to search for the optimal viewpoint of a scene with the highest aesthetic property. Using the information of the targets, we propose a novel method for predicting human aesthetic sense for a scene. We construct evaluation functions based on certain known composition rules using three factors, namely, target size, visual balance, and composition fitting value. Then a score, which is a reflection of human evaluation, will be obtained using these functions. The optimal viewpoint will be selected from a number of candidates around the target group, by evaluating the aesthetic properties of scenes for each candidate viewpoint. Finally, once the optimal viewpoint is confirmed, path planning and path following controls are implemented for the robots during the moving process.

[1] Faria J, Bagley S, Ruger S, et al. Challenges of finding aesthetically pleasing images. Proc. of the 14th IEEE International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), 2013, pp. 1-4.

[2] Dunstan B, Composing your paintings, Watson- Guptill Publications, 1971.

[3] Krages B, Photography: the art of composition, Skyhorse Publishing, Inc., 2012.

[4] Datta R, Joshi D, Li J, Studying Aesthetics in Photographic Images Using a Computational Approach, Proc. of the European Conference on Computer Vision, 2006, pp. 288-301.

[5] Wu Y, Bauckhage C, Thurau C, The good, the bad, and the ugly: Predicting aesthetic image labels, Proc. of the 20th IEEE International Conference on Pattern Recognition (ICPR), 2010, pp. 1586-1589.

[6] Wong L K, Low K, Saliency-enhanced image aesthetics class prediction, Proc. of the 16th IEEE International Conference on Image Processing (ICIP), 2009, pp. 997-1000.

[7] Ito M, Sekiyama K, Optimal viewpoint selection for cooperative visual assistance in multi-robot systems, Proc. of the 2015 IEEE/SICE International Symposium on System Integration (SII), 2015, pp. 605-610.

[8] Liu L, Chen R, Wolf L, et al. Optimizing photo composition. Computer Graphics Forum, Blackwell Publishing Ltd, Vol. 29, No. 2, 2010, pp. 469-478.

[9] Zhang F L,Wang M, Hu S M, Aesthetic Image Enhancement by Dependence-aware Object Recomposition, IEEE Transactions on Multimedia, vol. 15, no. 7, 2013, pp. 1480-1490.

[10] Byers Z, Dixon M, Goodier K, Grimm C, SmartW, An Autonomous Robot Photographer, Proc. of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, 2003, pp. 2636-2641.

[11] Byers Z, Dixon M, Smart W D, Grimm C, Say cheese! Experiences with a robot photographer. AI magazine, Vol. 25, No. 3, 2004, pp. 37-46.

[12] Toyama K, Krumm J, Brumitt B,Wallflower: Principles and practice of background maintenance, Proc. of the 7th IEEE International Conference on Computer Vision, 1999, pp. 255-261.

[13] Martinez B, Visual forces, an introduction to design, Pearson College Division, 1995.

[14] Maor E, Trigonometric delights, Princeton university press, 2013.

[15] Lan K, Sekiyama K, Autonomous Viewpoint Selection of Robots Based on Aesthetic Composition Evaluation of a Photo, Proc. of the 2015 IEEE Symposium Series on Computational Intelligence, 2015, pp. 295-300.

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

Journal Information

CiteScore 2017: 5.00

SCImago Journal Rank (SJR) 2017: 0.492
Source Normalized Impact per Paper (SNIP) 2017: 2.813

Cited By


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 136 136 23
PDF Downloads 46 46 7