[1] Tong H, Li M, Zhang H, et al. Classification of digital photos taken by photographers or home users[C]//Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing. Heidelberg:Springer-Verlag, 2004:198-205
[2] Ke Y, Tang X, Jing F. The design of high-level features for photo quality assessment[C]//Proceedings of the 19th IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway NJ:IEEE, 2006:419-426.
[3] Datta R, Joshi D, Li J, et al. Studying aesthetics in photographic images using a computational approach[C]//Proceedings of the 9th European Conference on Computer Vision. Heidelberg:Springer-Verlag, 2006:288-301.
[4] Luo Y, Tang X. Photo and video quality evaluation:Focusing on the subject[C]//Proceedings of the 10th European Conference on Computer Vision (ECCV). Heidelberg:Springer-Verlag, 2008:386-399.
[5] Wong L K, Low K L. Saliency-enhanced image aesthetics class prediction[C]//Proceedings of the 16th IEEE International Conference on Image Processing. Piscataway NJ:IEEE, 2009:997-1000.
[6] Li C, Chen T. Aesthetic visual quality assessment of paintings[J]. IEEE Journal of Selected Topics in Signal Processing, 2009, 3(2):236-252.
[7] Nishiyama M, Okabe T, Sato I, et al. Aesthetic quality classification of photographs based on color harmony[C]//Proceedings of the 24th IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway NJ:IEEE, 2011:33-40.
[8] Dhar S, Ordonez V, Berg T L. High level describable attributes for predicting aesthetics and interestingness[C]//Proceedings of the 24th IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway NJ:IEEE, 2011:1657-1664.
[9] Marchesotti L, Perronnin F, Larlus D, et al. Assessing the aesthetic quality of photographs using generic image descriptors[C]//Proceedings of the 13th IEEE International Conference on Computer Vision. Piscataway NJ:IEEE, 2011:1784-1791.
[10] Hurter B. The best of photographic lighting techniques and images for digital photographers[M]. 2nd ed. New York:Amherst Media, 2007.
[11] Hunter F, Biver S, Fuqua P. Light:Science and magic:An introduction to photographic lighting[M]. 3rd ed. New York:Focal Press, 2007.
[12] Grey C. Master lighting guide for portrait photographers[M]. New York:Amherst Media, 2004.
[13] Prakel D. Basics photography:Lighting[M]. Switzerland:AVA Publishing, 2007.
[14] Jin X, Zhao M, Chen X, et al. Learning artistic lighting template from portrait photographs[C]//Proceedings of the 11th European Conference on Computer Vision. Heidelberg:Springer-Verlag, 2010:101-114.
[15] Chen X, Jin X, Wu H, et al. Learning templates for artistic portrait lighting analysis[J]. IEEE Transactions on Image Processing, 2015, 24(2):608-618.
[16] Luo W, Wang X, Tang X. Content-based photo quality assessment[C]//Proceedings of the 13th IEEE International Conference on Computer Vision. Piscataway NJ:IEEE, 2011:2206-2213.
[17] Tang X, Luo W, Wang X. Content-based photo quality assessment[J]. IEEE Transactions on Multimedia, 2013, 15(8):1930-1943.
[18] Gray D, Yu K, Xu W, et al. Predicting facial beauty without landmarks[C]//Proceedings of the 11th European Conference on Computer Vision. Heidelberg:Springer-Verlag, 2010:434-447.
[19] Li C, Gallagher A, Loui A C, et al. Aesthetic quality assessment of consumer photos with faces[C]//Proceedings of the IEEE International Conference on Image Processing. Piscataway NJ:IEEE, 2010:3221-3224.
[20] Khan S S, Vogel D. Evaluating visual aesthetics in photographic portraiture[C]//Proceedings of the 8th Annual Symposium on Computational Aesthetics in Graphics, Visualization, and Imaging. New York:ACM, 2012:55-62.
[21] Lu X, Lin Z, Jin H, et al. RAPID:Rating pictorial aesthetics using deep learning[C]//Proceedings of the ACM International Conference on Multimedia. New York:ACM, 2014:457-466.
[22] Kao Y, Wang C, Huang K. Visual aesthetic quality assessment with a regression model[C]//Proceedings of 2015 IEEE International Conference on Image Processing. Piscataway NJ:IEEE, 2015. 1583-1587.
[23] Lu X, Lin Z, Shen X, et al. Deep multi-patch aggregation network for image style, aesthetics, and quality estimation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Piscataway NJ:IEEE, 2015:990-998.
[24] Dong Z, Tian X. Multi-level photo quality assessment with multi-view features[J]. Neurocomputing, 2015, 168:308-319.
[25] Wang W, Zhao M, Wang L, et al. A multi-scene deep learning model for image aesthetic evaluation[J]. Signal Processing Image Communication, 2016, 47(C):511-518.
[26] Kao Y, Huang K, Maybank S. Hierarchical aesthetic quality assessment using deep convolutional neural networks[J]. Signal Processing Image Communication, 2016, 47:500-510.
[27] Kong S, Shen X, Lin Z, et al. Photo aesthetics ranking network with attributes and content adaptation[C]//Proceedings of 14th European Conference on Computer Vision. Heidelberg:Springer-Verlag, 2016:662-679.
[28] Jin X, Chi J, Peng S, et al. Deep image aesthetics classification using inception modules and fine-tuning connected layer[C]//Proceedings of the 8th International Conference on Wireless Communications and Signal Processing. Piscataway NJ:IEEE, 2016, doi:10.1109/WCSP.2016.7752571.
[29] Ma S, Liu J, Chen C W. A-Lamp:Adaptive layout-aware multi-patch deep convolutional neural network for photo aesthetic assessment[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway NJ:IEEE, 2017:722-731.
[30] Jin X, Wu L, Li X D, et al. ILGNet:Inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation[J]. arXiv.org, 2018, arXiv:1610.02256v3.
[31] Jin B, Segovia M V O, Süsstrunk S. 2016. Image aesthetic predictors based on weighted CNNs[C]//Proceedings of 2016 IEEE International Conference on Image Processing. Piscataway NJ:IEEE, 2016:2291-2295.
[32] Wang Z, Liu D, Chang S, et al. Image aesthetics assessment using deep chatterjee's machine[C]//Proceedings of 2017 International Joint Conference on Neural Networks. Piscataway NJ:IEEE, 2017:941-948.
[33] Hou L, Yu C P, Samaras D. Squared earth mover's distancebased loss for training deep neural networks[J]. arXiv.org, 2017, arXiv:1611.05916
[34] Deng Y, Chen C L, Tang X. Image aesthetic assessment:An experimental survey[J]. IEEE Signal Processing Magazine, 2016, 34(4):80-106.
[35] Murray N, Marchesotti L, Perronnin F. AVA:A large-scale database for aesthetic visual analysis[C]//Proceedings of the 25th IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway NJ:IEEE, 2012:2408-2415.
[36] Wu O, Hu W M, Gao J. Learning to predict the perceived visual quality of photos[C]//Proceedings of 2011 IEEE International Conference on Computer Vision. Piscataway NJ:IEEE, 2011:225-232.
[37] Park T S, Zhang B T. Consensus analysis and modeling of visual aesthetic perception[J]. IEEE Transactions on Affective Computing, 2015, 6(3):272-285.
[38] Kim W H, Choi J H, Lee J S. Subjectivity in aesthetic quality assessment of digital photographs:Analysis of user comments[C]//Proceedings of the 23rd ACM international conference on Multimedia. New York:ACM, 2015:983-986.
[39] Kim W H, Choi J H, Lee J S. Objectivity and subjectivity in aesthetic quality assessment of digital photographs[J]. IEEE Transactions on Affective Computing, 2018, doi:10.1109/TAFFC.2018.2809752.
[40] Jin X, Wu L, Li X, et al. Predicting aesthetic score distribution through cumulative Jensen-Shannon divergence[C]//Proceedings of AAAI Conference on Artificial Intelligence (AAAI). New York:AAAI, 2018.
[41] Chang K Y, Lu K H, Chen C S. Aesthetic critiques generation for photos[C]//Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). Piscataway NJ:IEEE, 2017:3534-3543.
[42] Collomosse J, Tu B, Wilber M, et al. Sketching with Style:Visual search with sketches and aesthetic context[C]//Proceedings of IEEE International Conference on Computer Vision. Piscataway NJ:IEEE, 2017:2679-2687.
[43] Cui C, Fang H, Deng X, et al. Distribution-oriented aesthetics assessment for image search[C]//Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM, 2017:1013-1016.
[44] Wang W, Shen J. Deep cropping via attention box prediction and aesthetics assessment[C]//2017 IEEE International Conference on Computer Vision. Piscataway NJ:IEEE, 2017:2205-2213.
[45] Ren J, Shen X, Lin Z, et al. Personalized image aesthetics[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Piscataway NJ:IEEE, 2017:638-647.
[46] Schwarz K, Wieschollek P, Lensch H P A. Will people like your image?[J]. arXiv.org, 2018, arXiv:1611.05203.
[47] Wang W S, Yang S, Zhang W S, et al. Neural aesthetic image reviewer[J]. arXiv.org, 2018, arXiv:1802.10240.
[48] Yanulevskaya V, Uijlings J, Bruni E, et al. In the eye of the beholder:Employing statistical analysis and eye tracking for analyzing abstract paintings[C]//Proceedings of the 20th ACM International Conference on Multimedia. New York:ACM, 2012:349-358.
[49] Agrawal A, Premachandran V, Kakarala R. Rating image aesthetics using a crowd sourcing approach[C]//Workshops of the 6th Pacific-Rim Symposium on Image and Video Technology. New York:Springer-Verlag New York Inc., 2013, 8334:24-32.
[50] Datta R, Wang J Z. ACQUINE:Aesthetic quality inference engine-real time automatic ratings of photo aesthetics[C]//Proceedings of the ACM International Conference on Multimedia Information Retrieval. New York:ACM, 2010:421-424.
