Synthesized computational aesthetic evaluation of photos

Neurocomputing

Volumn 172, Issue , 2016, Pages 244-252

  Wang, Weining ^a   Cai, Dong ^a   Wang, Li ^a   Huang, Qinghua ^a   Xu, Xiangmin ^a   Li, Xuelong ^b  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

Aesthetic evaluation; Classification; Feature extraction; Mobile application; Photo

Indexed keywords

ARTIFICIAL INTELLIGENCE; BEHAVIORAL RESEARCH; COMPUTATION THEORY; COMPUTER VISION; EXTRACTION; FEATURE EXTRACTION; IMAGE PROCESSING; INFORMATION THEORY; LEARNING SYSTEMS; MOBILE DEVICES;

AESTHETIC EVALUATION; CLASSIFICATION ACCURACY; COMPUTATIONAL AESTHETICS; CONVENTIONAL MODELS; MOBILE APPLICATIONS; PHOTO; SVM-BASED CLASSIFIERS; SYSTEM ON MOBILE DEVICES;

CLASSIFICATION (OF INFORMATION);

ACCURACY; ARTICLE; CLASSIFICATION ALGORITHM; IMAGE ANALYSIS; IMAGE DISPLAY; IMAGE PROCESSING; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PREDICTION; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE;

EID: 84946497703     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.12.106     Document Type: Article

References (47)

1. F. Hoenig, Defining computational aesthetics, in: Proceedings of the First Eurographics Conference on Computational Aesthetics in Graphics, Visualization and Imaging, Computational Aesthetics'05, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 2005, pp. 13-18.
2. T. Mei, B. Wang, X.-S. Hua, H.-Q. Zhou, S. Li, Probabilistic multimodality fusion for event based home photo clustering, in: IEEE International Conference on Multimedia and Expo, 2006, pp. 1757-1760.
3. Zha Z.J., Wang M., Zheng Y.T., Yang Y., Hong R., Chua T.S. Interactive video indexing with statistical active learning. IEEE Trans. Multimedia 2012, 14(1):17-27.
4. Obrador P., de Oliveira R., Oliver N. Supporting personal photo storytelling for social albums. Proceedings of the International Conference on Multimedia, MM '10 2010, 561-570. ACM, New York, NY, USA.
5. Joshi D., Datta R., Fedorovskaya E., Luong Q.-T., Wang J., Li J., Luo J. Aesthetics and emotions in images. IEEE Signal Process. Mag. 2011, 28:94-115.
6. Bauerly M., Liu Y. Computational modeling and experimental investigation of effects of compositional elements on interface and design aesthetics. Int. J. Hum. Comput. Stud. 2006, 64:670-682.
7. Y. Ke, X. Tang, F. Jing, 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.
8. Luo Y., Tang X. Photo and video quality evaluation: Focusing on the subject. Computer Vision ECCV 2008, Volume 5304 of Lecture Notes in Computer Science 2008, 386-399. Springer, Berlin/Heidelberg. D. Forsyth, P. Torr, A. Zisserman (Eds.).
9. Datta R., Joshi D., Li J., Wang J. Studying aesthetics in photographic images using a computational approach. Comput. Vis. ECCV 2006, 3953(2006):288-301.
10. L. Marchesotti, F. Perronnin, D. Larlus, G. Csurka, Assessing the aesthetic quality of photographs using generic image descriptors, in: IEEE International Conference on Computer Vision (ICCV), 2011, pp. 1784-1791.
11. Li C., Chen T. Aesthetic visual quality assessment of paintings. IEEE J. Sel. Top. Signal Process. 2009, 3:236-252.
12. Li Q., Luo S., Shi Z. Fuzzy aesthetic semantics description and extraction for art image retrieval. Comput. Math. Appl. 2009, 57:1000-1009.
13. P. Obrador, L. Schmidt-Hackenberg, N. Oliver, The role of image composition in image aesthetics, in: 17th IEEE International Conference on Image Processing (ICIP), 2010, pp. 3185-3188.
14. Bhattacharya S., Sukthankar R., Shah M. A framework for photo-quality assessment and enhancement based on visual aesthetics. Proceedings of the International Conference on Multimedia 2010, 271-280. MM '10, ACM, New York, NY, USA.
15. Birkhoff G.D. Aesthetic Measure 1933, Harvard University Press, Cambridge, Massachusetts.
16. Machado P., Cardoso A. Computing aesthetics. Proceedings of the Brazilian Symposium on Artificial Intelligence 1998, 219-229. SBIA?8, Springer-Verlag.
17. Romero J., Machado P., Carballal A., Osorio O. Aesthetic classification and sorting based on image compression. Applications of Evolutionary Computation 2011, 394-403. Springer.
18. L.-K. Wong, K.-L. Low, Saliency-enhanced image aesthetics class prediction, in: 16th IEEE International Conference on Image Processing (ICIP), 2009, pp. 997-1000.
19. Rigau J., Feixas M., Sbert M. Informational aesthetics measures. IEEE Comput. Graphics Appl. 2008, 28:24-34.
20. R. Datta, J.Z. Wang, ACQUINE: aesthetic quality inference engine-real-time automatic rating of photo aesthetics, in: Multimedia Information Retrieval, pp. 421-424.
21. Yao L., Suryanarayan P., Qiao M., Wang J., Li J. OSCAR: on-site composition and aesthetics feedback through exemplars for photographers. Int. J. Comput. Vis. 2012, 96:353-383.
22. Jin Y., Wu Q., Liu L. Aesthetic photo composition by optimal crop-and-warp. Comput. Graphics 2012, 36:955-965.
23. Lo K.-Y., Liu K.-H., Chen C.-S. Intelligent photographing interface with on-device aesthetic quality assessment. Computer Vision-ACCV 2012 Workshops, Number 7729 in Lecture Notes in Computer Science 2013, 533-544. Springer, Berlin Heidelberg. J.-I. Park, J. Kim (Eds.).
24. W.-N. Wang, Y.-L. Yu, Image emotional semantic query based on color semantic description, in: Machine Learning and Cybernetics, in: Proceedings of 2005 International Conference on, vol. 7, 2005, pp. 4571-4576.
25. Ciesielski V., Barile P., Trist K. Finding image features associated with high aesthetic value by machine learning. Evolutionary and Biologically Inspired Music, Sound, Art and Design 2013, 47-58. Springer.
26. Daugman J.G. Spatial visual channels in the Fourier plane. Vis. Res. 1984, 24:891-910.
27. Tamura H., Mori S., Yamawaki T. Textural features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 1978, 8:460-473.
28. Haralick R.M., Shanmugam K., Dinstein I.H. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 1973, 610-621.
29. W. Luo, X. Wang, X. Tang, Content-based photo quality assessment, in: IEEE International Conference on Computer Vision (ICCV), 2011, pp. 2206-2213.
30. He K., Sun J., Tang X. Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33:2341-2353.
31. Li M., Chen X., Li X., Ma B., Vitányi P.M. The similarity metric. IEEE Trans. Inf. Theory 2004, 50:3250-3264.
32. Zurek W.H. Algorithmic randomness and physical entropy. Phys. Rev. A 1989, 40:4731.
33. Lind R.W. Attention and the aesthetic object. J. Aesthetics Art Critic 1980, 39:131-142.
34. Huang Q., Bai X., Li Y., Jin L., Li X. Optimized graph-based segmentation for ultrasound images. Neurocomputing 2014, 129:216-224.
35. F. Perazzi, P. Krahenbuhl, Y. Pritch, A. Hornung, Saliency filters: Contrast based filtering for salient region detection, in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 733-740.
36. Cortes C., Vapnik V. Support-vector networks. Mach. Learn. 1995, 20:273-297.
37. Smola A.J., Schölkopf B. A tutorial on support vector regression,. Stat. Comput. 2004, 14(3):199-222.
38. Chang C.-C., Lin C.-J. Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol 2011, 27(1-27):27. 2.
39. I.H. Witten, E. Frank, L.E. Trigg, M.A. Hall, G. Holmes, S.J. Cunningham, Weka: Practical Machine Learning Tools and Techniques with Java Implementations,1999.
40. Huang Q., Tao D., Li X., Jin L., Wei G. Exploiting local coherent patterns for unsupervised feature ranking. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2011, 41(6):1471-1482.
41. Li Y., Liu W., Li X., Huang Q., Li X. GA-SIFT: a new scale invariant feature transform for multispectral image using geometric algebra. Inf. Sci. 2014, 281:559-572.
42. Zha Z.J., Yang L., Mei T., Wang M., Wang Z., Chua T.S., Hua X.S. Visual query suggestion: towards capturing user intent in internet image search. ACM Trans. Multimedia Comput. Commun. Appl. (TOMCCAP) 2010, 6(3):13-19.
43. Itti L., Koch C. Computational modelling of visual attention. Nat. Rev. Neurosci. 2001, 2:194-203.
44. http://www.photo.net.
45. Guo L.H., Xiong Y.C., Huang Q.H., Li X.L. Image aesthetic assessment using both hand-crafting and semantic features. Neurocomputing 2014, 143:14-26.
46. Huang Q.H., Yang F.B., Liu L.Z., Li X.L. Automatic segmentation of breast lesions for intelligent interaction in ultrasonic computer-aided diagnosis. Inf. Sci. 2015, 314:293-310.
47. Huang Q.H., Chen B.S., Wang J.D., Mei T. Personalized video recommendation through graph propagation. ACM Trans. Multimedia Comput. Commun. Appl. 2014, 10(4):32. Article.