Artificial Color Constancy via GoogLeNet with Angular Loss Function

References

• Barnard, K. Improvements to gamut mapping colour constancy algorithms. European Conference on Computer Vision. 2000, Berlin; pp. 390–403.
   Google Scholar
• Barron, J. T. Convolutional color constancy. International Conference on Computer Vision. 2015, Santiago; pp. 379–87.
   Google Scholar
• Bianco, S., C. Cusano, P. Napoletano, and R. Schettini. 2017. Improving CNN-based texture classification by color balancing. Journal of Imaging 3 (33). doi:10.3390/jimaging3030033.
   Google Scholar
• Bianco, S., C. Cusano, and R. Schettini Color constancy using CNNs. Computer Vision and Pattern Recognition Workshops. 2015a; pp. 81–89. doi:10.3389/fnbeh.2015.00081.
   Google Scholar
• Bianco, S., C. Cusano, and R. Schettini. 2015b. Single and multiple illuminant estimation using convolutional neural networks. ArXiv E-prints 1508.00998.
   Google Scholar
• Bianco, S., and R. Schettini. Color constancy using faces. In Computer vision and pattern recognition. 2012, Providence; pp. 65–72.
   Google Scholar
• Brainard, D. H., and B. A. Wandell. 1986. Analysis of the retinex theory of color vision. Journal of the Optical Society of America A 3 (10):1651–61. doi:10.1364/JOSAA.3.001651.
   PubMedGoogle Scholar
• Buchsbaum, G. 1980. A spatial processor model for object colour perception. Journal of the Franklin Institute 310 (1):1–26. doi:10.1016/0016-0032(80)90058-7.
   Web of Science ®Google Scholar
• Cardei, V. C., B. Funt, and K. Barnard. 2002. Estimating the scene illumination chromaticity by using a neural network. Journal of the Optical Society of America A 19 (12):2374–86. doi:10.1364/JOSAA.19.002374.
   Google Scholar
• Cheng, D., D. K. Prasad, and M. S. Brown. 2014. Illuminant estimation for color constancy: Why spatial-domain methods work and the role of the color distribution. Journal of the Optical Society of America 31 (5):1049–58. doi:10.1364/JOSAA.31.001049.
   Web of Science ®Google Scholar
• Cheng, D., B. Price, S. Cohen, and M. S. Brown. 2015. Effective learning-based illuminant estimation using simple features. In Computer vision and pattern recognition, 1000–08.
   Google Scholar
• Ciurea, F., and B. Funt A large image database for color constancy research. In Imaging Science and Technology Eleventh Color Imaging Conference. 2003, Scottsdale; pp. 160–64.
   Google Scholar
• Elfiky, N., T. Gevers, A. Gijsenij, and J. Gonzalez. 2014. Color constancy using 3D scene geometry derived from a single image. In IEEE Transactions on Image Processing 23 (9):3855–68. doi:10.1109/TIP.83.
   PubMed Web of Science ®Google Scholar
• Fei-Fei, L., R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. In Computer vision and pattern recognition. 2004, Washington, DC; pp. 178–178.
   Google Scholar
• Finlayson, G., and E. Trezzi Shades of gray and colour constancy. Color Imaging Conference. 2004; pp. 37–41. doi:10.1080/13557850410001673987.
   Google Scholar
• Finlayson, G. D. Corrected-moment illuminant estimation. International Conference on Computer Vision. 2013, Sydney; pp. 1904–11.
   Google Scholar
• Finlayson, G. D., S. D. Hordley, and P. M. Hubel. 2001. Color by correlation: A simple, unifying framework for color constancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (11):1209–21. doi:10.1109/34.969113.
   Web of Science ®Google Scholar
• Finlayson, G. D., S. D. Hordley, and I. Tastl Gamut constrained illuminant estimation. International Conference on Computer Vision. 2003; 2:792–99. doi:10.1038/nmat1018.
   Google Scholar
• Fourure, D., R. Emonet, E. Fromont, D. Muselet, A. Trémeau, and C. Wolf Mixed pooling neural networks for color constancy. IEEE International Conference on Image Processing. 2016.
   Google Scholar
• Funt, B., and W. Xiong. 2004. Estimating illumination chromaticity via support vector regression. In Color and Imaging Conference 50 (8):341–48.
   Google Scholar
• Gao, S., W. Han, K. Yang, C. Li, and Y. Li. 2014. Efficient color constancy with local surface reflectance statistics. In European Conference on Computer Vision 8690:158–73.
   Google Scholar
• Gehler, P. V., C. Rother, A. Blake, T. Minka, and T. Sharp. Bayesian color constancy revisited. In Computer vision and pattern recognition. 2008, Anchorage; pp. 1–8.
   Google Scholar
• Gijsenij, A., and T. Gevers. 2011. Color constancy using natural image statistics and scene semantics. IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (4):687–98. doi:10.1109/TPAMI.2010.93.
   PubMed Web of Science ®Google Scholar
• Hara, K., R. Vemulapalli, and R. Chellappa. 2017. Designing deep convolutional neural networks for continuous object orientation estimation. ArXiv E-prints 1702.01499.
   Google Scholar
• Hordley, S., and G. Finlayson Re-evaluating color constancy algorithms. International Conference on Pattern Recognition. 2004, Cambridge; pp. 76–79.
   Google Scholar
• Hosseini, H., and R. Poovendran. 2018. Semantic adversarial examples. ArXiv E-prints 1804.00499.
   Google Scholar
• Hu, Y., B. Wang, and S. Lin. FC^4: Fully convolutional color constancy with confidence-weighted pooling. In Computer vision and pattern recognition. 2017, Honolulu; pp. 330–39.
   Google Scholar
• Joze, H. R. V., and M. S. Drew. 2014. Exemplar-based color constancy and multiple illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (5):860–73. doi:10.1109/TPAMI.2013.169.
   PubMed Web of Science ®Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems 1:1097–105.
   Google Scholar
• Lou, Z., T. Gevers, N. Hu, and M. P. Lucassen Color constancy by deep learning. British Machine Vision Conference. 2015, Swansea.
   Google Scholar
• Lynch, S. E., M. S. Drew, and G. D. Finlayson. 2013. Colour constancy from both sides of the shadow edge. IEEE ICCV Workshops 1:899–906.
   Google Scholar
• Prinet, V., D. Lischinski, and M. Werman Illuminant chromaticity from image sequences. International Conference on Computer Vision. 2013, Sydney.
   Google Scholar
• Qian, Y., K. Chen, J. Nikkanen, J. K. Kämäräinen, and J. Matas Recurrent color constancy. International Conference on Computer Vision. 2017, Venice; pp. 5459–67.
   Google Scholar
• Rosenberg, C., M. Hebert, and S. Thrun Color constancy using kl-divergence. International Conference on Computer Vision. 2001, Vancouver; 1:239–46.
   Google Scholar
• Shi, W., C. C. Loy, and X. Tang Deep specialized network for illuminant estimation. European Conference on Computer Vision, 2016, Amsterdam; pp. 371–87.
   Google Scholar
• Simonyan, K., and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. ArXiv E-prints 1409.1556.
   Google Scholar
• Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Computer Vision and Pattern Recognition. 2015, Boston; pp. 1–9.
   Google Scholar
• Van de Weijer, J., T. Gevers, and A. Gijsenij. 2007. Edge-based color constancy. IEEE Transactions on Image Processing 16 (9):2207–14. doi:10.1109/TIP.2007.901808.
   PubMed Web of Science ®Google Scholar
• Von Kries, J. 1970. Influence of adaptation on the effects produced by luminous stimuli. In sources of color science. In Handbook of stuff I care about, ed. D. L. MacAdam, 120–26. Cambridge, MA: MIT Press.
   Google Scholar
• Yang, K.-F., S.-B. Gao, and Y.-J. Li. Efficient illuminant estimation for color constancy using grey pixels. In Computer Vision and Pattern Recognition. 2015, Boston; pp. 2254–63.
   Google Scholar