Artificial Color Constancy via GoogLeNet with Angular Loss Function

Figures & data

Figure 1. Classification of images by VGG-16 net. Top row: original images from Caltech 101 dataset (Fei-Fei, Fergus, and Perona 2004); bottom row: the same images casted by random uniform illumination.

Figure 2. Schematic representation of the GoogLeNet. Credits (Szegedy et al. 2015).

Figure 3. Plots of $y=1-cos\epsilon$ (solid line) and $y=1-{cos}^2\epsilon$ (dashed line).

Table 1. The results obtained on SFU Grayball dataset, and comparison with state-of-the-art methods. First two sections correspond to statistic-based and learning-based methods.

	Mean	Median	Standard Deviation	Best 25%	Worst 25%	95th Percentile
Gray World, Buchsbaum (1980)	7.9	7.0	-	-	15.2	-
White Patch, Brainard and Wandell (1986)	6.8	5.3	-	-	-	-
Shades-of-Gray, Finlayson and Trezzi (2004)	6.1	5.3	-	-	-	-
2nd-order Gray-Edge, Van de Weijer, Gevers, and Gijsenij (2007)	6.1	4.3	-	-	-	-
Gray Patches, Yang, Gao, and Li (2015)	6.1	4.6	-	1.1	13.6	-
Local Surface Refl. Gao et al. (2014)	6.0	5.1	-	-	11.9	-
1st-order-Gray-Edge, Van de Weijer, Gevers, and Gijsenij (2007)	5.9	4.7	-	-	-	-
3D Scene Geometry, Elfiky et al. (2014)	5.4	4.5	-	-	-	-
Temporal sequence, Prinet, Lischinski, and Werman (2013)	5.4	4.6	-	-	-	-
Single max pooling, Fourure et al. (2016)	5.2	4.5	-	-	-	-
Mixed MaxL (Bianco, Cusano, and Schettini 2015b) pooling, Fourure et al. (2016)	4.9	4.3	-	-	-	-
Exemplar-based, Joze and Drew (2014)	4.4	3.3	-	-	-	-
AlexNet Retrained, Lou et al. (2015)	3.9	3.0	3.3	-	-	-
GoogLeNet + MSE	2.55	1.91	2.19	0.58	5.59	6.78
GoogLeNet + angular loss	1.98	1.49	1.87	0.39	4.51	5.73

Table 2. The results obtained on reprocessed ColorChecker dataset, and comparison with state-of-the-art methods. First two sections correspond to statistic-based and learning-based methods.

	Mean	Median	Trimean	Best 25%	Worst 25%	95th Percentile
White Patch, Brainard and Wandell (1986)	7.55	5.68	6.35	1.45	16.12	-
Edge-based Gamut, Barnard (2000)	6.52	5.04	5.43	1.90	13.58	-
Gray World, Buchsbaum (1980)	6.36	6.28	6.28	2.33	10.58	11.3
Second-order Gray-Edge, Van de Weijer, Gevers, and Gijsenij (2007)	5.13	4.44	4.62	2.11	9.26	-
Shades-of-Gray, Finlayson and Trezzi (2004)	4.93	4.01	4.23	1.14	10.20	11.9
Pixel-based Gamut, Barnard (2000)	4.20	2.33	2.91	0.50	10.72	14.1
Natural Image Statistics, Gijsenij and Gevers (2011)	4.19	3.13	3.45	1.00	9.22	11.7
19 Edge Moments, Finlayson (2013)	2.80	2.00	-	-	-	6.90
Mixed MaxL (Bianco, Cusano, and Schettini 2015b) pooling, Fourure et al. (2016)	3.33	2.63	-	-	-	-
Single max pooling, Fourure et al. (2016)	3.31	2.59	-	-	-	-
Exemplar-based, Joze and Drew (2014)	3.10	2.30	-	-	-	-
AlexNet Retrained, Lou et al. (2015)	3.1	2.30	-	-	-	-
Patch-CNN, Bianco, Cusano, and Schettini (2015a)	2.63	1.98	-	-	-	-
CCC, Barron (2015)	1.95	1.22	1.38	0.35	4.76	5.85
DS-Net (HypNet+SelNet), Shi, Loy, and Tang (2016)	1.90	1.12	1.33	0.31	4.84	5.99
SqueezeNet-FC^4, Hu, Wang, and Lin (2017)	1.65	1.18	1.27	0.38	3.78	4.73
GoogLeNet + MSE	3.06	2.15	2.32	0.48	7.01	9.64
GoogLeNet + angular loss	2.55	1.69	1.92	0.39	6.03	6.7

Figure 4. An example of images from Grayball dataset before (left) and after (right) removing illumination color cast using the algorithm presented in this paper.

*Figures are given separately

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

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

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

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

Finlayson, G., and E. Trezzi Shades of gray and colour constancy. Color Imaging Conference. 2004; pp. 37–41. doi:10.1080/13557850410001673987.

 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

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

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

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

Prinet, V., D. Lischinski, and M. Werman Illuminant chromaticity from image sequences. International Conference on Computer Vision. 2013, Sydney.

 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

Bianco, S., C. Cusano, and R. Schettini. 2015b. Single and multiple illuminant estimation using convolutional neural networks. ArXiv E-prints 1508.00998.

 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

Lou, Z., T. Gevers, N. Hu, and M. P. Lucassen Color constancy by deep learning. British Machine Vision Conference. 2015, Swansea.

 Google Scholar

Barnard, K. Improvements to gamut mapping colour constancy algorithms. European Conference on Computer Vision. 2000, Berlin; pp. 390–403.

 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

Finlayson, G. D. Corrected-moment illuminant estimation. International Conference on Computer Vision. 2013, Sydney; pp. 1904–11.

 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

Barron, J. T. Convolutional color constancy. International Conference on Computer Vision. 2015, Santiago; pp. 379–87.

 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

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