A Benchmark Image Set for Evaluating Stylization

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Reference

David Mould and Paul L. Rosin: A Benchmark Image Set for Evaluating Stylization. In: Computational Aesthetics 2016 CAE'16, 11-20.

DOI

http://dx.doi.org/10.2312/exp.20161059

Abstract

The non-photorealistic rendering community has had difficulty evaluating its research results. Other areas of computer graphics, and related disciplines such as computer vision, have made progress by comparing algorithms' performance on common datasets, or benchmarks. We argue for the benefits of establishing a benchmark image set to which image stylization methods can be applied, simplifying the comparison of methods, and broadening the testing to which a given method is subjected. We propose a preliminary set of benchmark images, representing a range of possible subject matter and image features of interest to researchers, and we describe the policies, tradeoffs, and reasoning that led us to the particular images in the set.

Extended Abstract

Bibtex

@inproceedings{Mould:2016:BIS:2981324.2981327,
author = {Mould, David and Rosin, Paul L.},
title = {A Benchmark Image Set for Evaluating Stylization},
booktitle = {Proceedings of the Joint Symposium on Computational Aesthetics and Sketch Based Interfaces and Modeling and Non-Photorealistic Animation and Rendering},
series = {Expresive '16},
year = {2016},
location = {Lisbon, Portugal},
pages = {11--20},
numpages = {10},
url = {http://dx.doi.org/10.2312/exp.20161059  http://dl.acm.org/citation.cfm?id=2981324.2981327 http://de.evo-art.org/index.php?title=A_Benchmark_Image_Set_for_Evaluating_Stylization},
ISSN = {1816-0859},
ISBN = {978-3-03868-000-0},
acmid = {2981327},
publisher = {Eurographics Association},
address = {Aire-la-Ville, Switzerland, Switzerland},
} 


Used References

1 Zainab AlMeraj , Brian Wyvill , Tobias Isenberg , Amy A. Gooch , Richard Guy, Computational Aesthetics 2008: Automatically mimicking unique hand-drawn pencil lines, Computers and Graphics, v.33 n.4, p.496-508, August, 2009 http://dx.doi.org/10.1016/j.cag.2009.04.004

2 {BK14} Bahrami K., Kot A. C.: A fast approach for no-reference image sharpness assessment based on maximum local variation. IEEE Signal Processing Letters 21, 6 (2014), 751--755. 5

3 Konstantinos Bousmalis , Marc Mehu , Maja Pantic, Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues, databases, and tools, Image and Vision Computing, v.31 n.2, p.203-221, February, 2013 http://dx.doi.org/10.1016/j.imavis.2012.07.003

4 {Bro66} Brodatz P.: Textures: a photographic album for artists and designers. Dover Publications, New York, 1966. 2

5 Christos Gatzidis , Stylianos Papakonstantinou , Vesna Brujic-Okretic , Stuart Baker, Recent Advances in the User Evaluation Methods and Studies of Non-Photorealistic Visualization and Rendering Techniques, Proceedings of the 2008 12th International Conference Information Visualisation, p.475-480, July 09-11, 2008 http://dx.doi.org/10.1109/IV.2008.75

6 Bruce Gooch , Erik Reinhard , Amy Gooch, Human facial illustrations: Creation and psychophysical evaluation, ACM Transactions on Graphics (TOG), v.23 n.1, p.27-44, January 2004 http://doi.acm.org/10.1145/966131.966133

7 Aaron Hertzmann, Non-Photorealistic Rendering and the science of art, Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering, June 07-10, 2010, Annecy, France http://doi.acm.org/10.1145/1809939.1809957

8 {HL13} Hall P., Lehmann A.-S.: Don't measure -- appreciate! NPR seen through the prism of art history. In Image and Video-Based Artistic Stylisation, Rosin P. L., Collomosse J. P., (Eds.). Springer, 2013, pp. 333--351. 1

9 {HS03} Hasler D., Süsstrunk S.: Measuring colourfulness in natural images. In Proc. SPIE Human Vision and Electronic Imaging (2003), pp. 87--95. 4

10 John Immerkær, Fast Noise Variance Estimation, Computer Vision and Image Understanding, v.64 n.2, p.300-302, Sept. 1996 http://dx.doi.org/10.1006/cviu.1996.0060

11 Tobias Isenberg , Petra Neumann , Sheelagh Carpendale , Mario Costa Sousa , Joaquim A. Jorge, Non-photorealistic rendering in context: an observational study, Proceedings of the 4th international symposium on Non-photorealistic animation and rendering, June 05-07, 2006, Annecy, France http://doi.acm.org/10.1145/1124728.1124747

12 {Ise13} Isenberg T.: Evaluating and validating non-photorealistic and illustrative rendering. In Image and Video-Based Artistic Stylisation, Rosin P. L., Collomosse J. P., (Eds.). Springer, 2013, pp. 311--331. 1, 2

13 Penousal Machado , Amílcar Cardoso, Computing Aethetics, Proceedings of the 14th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence, p.219-228, November 04-06, 1998 http://dl.acm.org/citation.cfm?id=669151&CFID=558819604&CFTOKEN=68186175

14 Anish Mittal , Anush Krishna Moorthy , Alan Conrad Bovik, No-Reference Image Quality Assessment in the Spatial Domain, IEEE Transactions on Image Processing, v.21 n.12, p.4695-4708, December 2012 http://dx.doi.org/10.1109/TIP.2012.2214050

15 Florent Perronnin, AVA: A large-scale database for aesthetic visual analysis, Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.2408-2415, June 16-21, 2012 http://dl.acm.org/citation.cfm?id=2354807&CFID=558819604&CFTOKEN=68186175

16 Krešimir Matković , László Neumann , Attila Neumann , Thomas Psik , Werner Purgathofer, Global contrast factor - a new approach to image contrast, Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging, May 18-20, 2005, Girona, Spain http://dx.doi.org/10.2312/COMPAESTH/COMPAESTH05/159-167

17 David Mould, Authorial subjective evaluation of non-photorealistic images, Proceedings of the Workshop on Non-Photorealistic Animation and Rendering, August 08-10, 2014, Vancouver, British Columbia, Canada http://doi.acm.org/10.1145/2630397.2630400

18 {OBPS15} Orenstein E. C., Beijbom O., Peacock E. E., Sosik H. M.: WHOI-Plankton -- A large scale fine grained visual recognition benchmark dataset for plankton classification. CoRR abs/1510.00745 (2015). 2

19 Olga Russakovsky , Jia Deng , Hao Su , Jonathan Krause , Sanjeev Satheesh , Sean Ma , Zhiheng Huang , Andrej Karpathy , Aditya Khosla , Michael Bernstein , Alexander C. Berg , Li Fei-Fei, ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, v.115 n.3, p.211-252, December 2015 http://dx.doi.org/10.1007/s11263-015-0816-y

20 Paul L. Rosin , Yu-Kun Lai, Artistic minimal rendering with lines and blocks, Graphical Models, v.75 n.4, p.208-229, July, 2013 http://dx.doi.org/10.1016/j.gmod.2013.03.004]

21 {RRAJ15} Redi M., Rasiwasia N., Aggarwal G., Jaimes A.: The beauty of capturing faces: Rating the quality of digital portraits. In Conf. on Automatic Face and Gesture Recognition (2015), pp. 1--8. 5

22 Terence Sim , Simon Baker , Maan Bsat, The CMU Pose, Illumination, and Expression Database, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.25 n.12, p.1615-1618, December 2003 http://dx.doi.org/10.1109/TPAMI.2003.1251154

23 Bart Thomee , David A. Shamma , Gerald Friedland , Benjamin Elizalde , Karl Ni , Douglas Poland , Damian Borth , Li-Jia Li, YFCC100M: the new data in multimedia research, Communications of the ACM, v.59 n.2, February 2016 http://doi.acm.org/10.1145/2812802

24 Holger Winnemöller , Sven C. Olsen , Bruce Gooch, Real-time video abstraction, ACM Transactions on Graphics (TOG), v.25 n.3, July 2006 http://doi.acm.org/10.1145/1141911.1142018

25 {XHE*10} Xiao J., Hays J., Ehinger K. A., Oliva A., Torralba A.: SUN database: Large-scale scene recognition from abbey to zoo. In Conference on Computer Vision and Pattern Recognition (2010), pp. 3485--3492. 2

26 Mingtian Zhao , Song-Chun Zhu, Abstract painting with interactive control of perceptual entropy, ACM Transactions on Applied Perception (TAP), v.10 n.1, p.1-21, February 2013 http://doi.acm.org/10.1145/2422105.2422110


Links

Full Text

intern file

Sonstige Links

http://dl.acm.org/citation.cfm?id=2981327&CFID=558819604&CFTOKEN=68186175