Abstract
With the prevalence of mobile imaging devices, large amount of photos are produced in each day. Automatic image enhancing models, such as exemplar-based color correction model, are highly needed. Based on feature correspondence between the exemplars and the target photo, the model optimizes the correction parameters by solving a matrix factorization problem. However, current models do not consider how to obtain reliable exemplars. In this paper, a simple but effective idea is employed to address this issue. We introduce an aesthetics evaluation stage, which measures the quality of the exemplars, to only select aesthetically good exemplars into the color correction model. This pre-selection strategy makes the exemplars more reliable in the correction model, and thus improves the visual quality of the results. Visual and quantitative experiments validate our improved model.
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References
Zhu, W., Cui, P., Wang, Z., Hua, G.: Multimedia big data computing. IEEE Multimed. 22(3), 96–100 (2015)
Xiaojie, G., Li, Y., Ling, H.: LIME: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2016)
Hao, S., Guo, Y., Hong, R., Wang, M.: Scale-aware spatially guided mapping by combining multiple level-of-details. IEEE Multimed. 23(3), 34–42 (2016)
Hong, R., Zhang, L., Tao, D.: Unified photo enhancement by discovering aesthetic communities from Flickr. IEEE Trans. Image Process. 25(3), 1124–1135 (2016)
HaCohen, Y., Shechtman, E., Goldman, D., Lischinski, D.: Optimizing color consistency in photo collections. ACM Trans. Graph. (TOG) 32(4), 38 (2013)
Park, J., Tai, Y., Sinha, S., So Kweon, I.: Efficient and robust color consistency for community photo collections. In: Proceedings of CVPR (2016)
Lischinski, D., Farbman, Z., Uyttendaele, M., Szeliski, R.: Interactive local adjustment of tonal values. ACM Trans. Graph. (TOG) 25(3), 646–653 (2006)
Hong, R., Zhang, L., Zhang, C., Zimmermann, R.: Flickr circles: aesthetic tendency discovery by multi-view regularized topic modeling. IEEE Trans. Multimed. 18(8), 1555–1567 (2016)
Gijsenij, A., Gevers, T.: Color constancy using natural image statistics and scene semantics. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 687–698 (2011)
Zhang, H., Shang, X., Luan, H., Wang, M., Chua, T.-S.: Learning from collective intelligence: feature learning using social images and tags. ACM Trans. Multimed. Comput. Commun. Appl. 13(1), 1–23 (2016). article 1
Joshi, D., Datta, R., Luong, Q.-T., Fedorovskaya, E., Wang, J.Z., Li, J., Luo, J.: Aesthetics and emotions in images: a computational perspective. IEEE Sig. Process. Mag. 28(5), 94–115 (2011)
Zhang, H., Zha, Z.-J., Yang, Y., Yan, S., Gao, Y., Chua, T.-S.: Attribute-augmented semantic hierarchy: towards bridging semantic gap and intention gap in image retrieval. In: Proceedings of ACM Multimedia (2013)
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). https://doi.org/10.1007/11744078_23
Hong, R., Yang, Y., Wang, M., Hua, X.-S.: Learning visual semantic relationships for efficient visual retrieval. IEEE Trans. Big Data 1(4), 152–161 (2015)
Liu, X., Wang, M., Yin, B.-C., Huet, B., Li, X.: Event-based media enrichment using an adaptive probabilistic hypergraph model. IEEE Trans. Cybern. 45(11), 2461–2471 (2015)
Zhang, H., Kyaw, Z., Chang, S.-F., Chua, T.-S.: Visual translation embedding network for visual relation detection. In: Proceedings of CVPR (2017)
Xin, L., Lin, Z., Jin, H., Yang, J., Wang, J.: Rating image aesthetics using deep learning. IEEE Trans. Multimed. 17(11), 2021–2034 (2015)
Kong, S., Shen, X., Lin, Z., Mech, R., Fowlkes, C.: Photo aesthetics ranking network with attributes and content adaptation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 662–679. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_40
Zhang, H., Shen, F., Liu, W., He, X., Luan, H., Chua, T.-S.: Discrete collaborative filtering. In: Proceeding of SIGIR (2016)
Zhang, H., Zha, Z.-J., Yang, Y., Yan, S., Chua, T.-S.: Robust (semi) nonnegative graph embedding. IEEE Trans. Image Process. 23(7), 2996–3012 (2014)
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Zhou, Z., Hao, S., Liu, M. (2018). Exemplar-Based Photo Color Correction by Exploring Visual Aesthetics. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_31
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DOI: https://doi.org/10.1007/978-981-10-8530-7_31
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