Abstract
Digital facial portrait photographs make up a massive portion of photos in the web. A number of methods for evaluating the aesthetics of photographs have been proposed recently. However, there have been a little work in the research community to address the aesthetics of targeted image domain, such as portraits. This paper introduces a new compositional-based augmentation scheme for aesthetic evaluation of portraits by well-known deep convolutional neural network (DCNN) models. We present a set of feature augmentation methods that take into account compositional photographic rules to ensure that the aesthetic in portraits are not hindered by standard transformations used for DCNN models. On a portrait subset of the large-scale AVA dataset, the proposed approach demonstrated a reasonable improvement in classification performance over the baseline and vanilla deep learning approaches.
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Notes
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This size was roughly determined by observation. Most of the other images appear to have substantially larger faces.
References
Bakhshi, S., Shamma, D.A., Gilbert, E.: Faces engage us: photos with faces attract more likes and comments on instagram. In: Proceedings of the 32nd Annual ACM Conference on Human factors in Computing Systems, pp. 965–974. ACM (2014)
Krages, B.: Photography: The Art of Composition. Skyhorse Publishing Inc., New York (2012)
Khan, S.S., Vogel, D.: Evaluating visual aesthetics in photographic portraiture. In: Proceedings of the Eighth Annual Symposium on Computational Aesthetics in Graphics, Visualization, and Imaging, pp. 55–62. Eurographics Association (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint (2013). arXiv:1312.6229
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
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). doi:10.1007/11744078_23
Marchesotti, L., Perronnin, F., Larlus, D., Csurka, G.: Assessing the aesthetic quality of photographs using generic image descriptors. In: 2011 International Conference on Computer Vision, pp. 1784–1791. IEEE (2011)
Murray, N., Marchesotti, L., Perronnin, F.: AVA: A large-scale database for aesthetic visual analysis. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2408–2415. IEEE (2012)
Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J.Z.: RAPID: rating pictorial aesthetics using deep learning. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 457–466. ACM (2014)
Tang, X., Luo, W., Wang, X.: Content-based photo quality assessment. IEEE Trans. Multimed. 15, 1930–1943 (2013)
Tian, X., Dong, Z., Yang, K., Mei, T.: Query-dependent aesthetic model with deep learning for photo quality assessment. IEEE Trans. Multimed. 17, 2035–2048 (2015)
Redi, M., Rasiwasia, N., Aggarwal, G., Jaimes, A.: The beauty of capturing faces: rating the quality of digital portraits. In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 1, pp. 1–8 (2015)
Dpchallenge - a digital photography contest. http://www.dpchallenge.com/. Accessed 26 Aug 2016
Wang, Z., Dolcos, F., Beck, D., Chang, S., Huang, T.S.: Brain-inspired deep networks for image aesthetics assessment. arXiv preprint (2016). arXiv:1601.04155
Lihua, G., Fudi, L.: Image aesthetic evaluation using paralleled deep convolution neural network. arXiv preprint (2015). arXiv:1505.05225
Lienhard, A., Ladret, P., Caplier, A.: Low level features for quality assessment of facial images. In: 10th International Conference on computer Vision Theory and Applications, VISAPP, pp. 545–552 (2015)
Kalayeh, M.M., Seifu, M., LaLanne, W., Shah, M.: How to take a good selfie? In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 923–926 (2015)
Obrador, P., Schmidt-Hackenberg, L., Oliver, N.: The role of image composition in image aesthetics. In: IEEE International Conference on Image Processing, pp. 3185–3188 (2010)
Face++: Leading Face Recognition on Cloud. http://www.faceplusplus.com/. Accessed 26
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
ImageNet Database and Challenge. http://www.image-net.org/. Accessed 26
Sklearn-theano library. https://sklearn-theano.github.io. Acceseed 26
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. arXiv preprint (2014). arXiv:1405.3531
Child, J.: Studio Photography: Essential Skills. CRC Press, Boca Raton (2013)
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Kairanbay, M., See, J., Wong, LK. (2017). Aesthetic Evaluation of Facial Portraits Using Compositional Augmentation for Deep CNNs. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_34
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