# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 4 Experimental Results
### Experiment C: Cross-Dataset, Cross-Camera Testing
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Recovery AE**} & \multicolumn{8}{c}{**Reproduction AE**} \\ \cline{2-13}  & **Mean** & **Med.** & **Tri.** & **B25\%** & **W25\%** & **Max** & **Mean** & **Med.** & **Tri.** & **B25\%** & **W25\%** & **Max** \\ \hline GW [4; 5] & 4.41 & 3.05 & 3.36 & 0.68 & 10.53 & 39.54 & 5.56 & 4.00 & 4.39 & 0.88 & 12.89 & 42.57 \\ WP [6] & 3.57 & 2.30 & 2.57 & 0.52 & 8.89 & 23.44 & 4.39 & 3.00 & 3.30 & 0.66 & 10.58 & 31.12 \\ SoG [7] & 4.85 & 2.83 & 3.47 & 0.53 & 12.38 & 35.75 & 5.65 & 3.59 & 4.29 & 0.67 & 13.81 & 26.08 \\ GE1 [8] & 4.42 & 2.88 & 3.36 & 0.60 & 10.71 & 32.43 & 5.40 & 3.71 & 4.26 & 0.74 & 12.69 & 27.41 \\ GE2 [8] & 4.82 & 2.77 & 3.36 & 0.65 & 12.37 & 37.27 & 5.58 & 3.46 & 4.12 & 0.81 & 13.72 & 31.32 \\ PCA-based [21] & 5.39 & 3.15 & 3.87 & 0.81 & 13.49 & 45.97 & 6.19 & 4.01 & 4.74 & 1.00 & 14.69 & 32.77 \\ GGW [8] & 3.40 & 2.00 & 2.34 & 0.45 & 8.77 & 28.60 & 4.21 & 2.64 & 3.04 & 0.57 & 10.61 & 40.59 \\ LSR [44] & 4.80 & 3.78 & 4.10 & 1.35 & 10.00 & 20.48 & 5.96 & 4.79 & 5.17 & 1.59 & 12.22 & 21.47 \\ Luo [19] & **1.97** & **1.30** & **1.45** & **0.36** & 4.75 & 25.38 & **2.57** & **1.64** & **1.86** & **0.45** & 6.32 & 37.14 \\ \hline \multicolumn{13}{c}{**Proposed**} \\ \hline ClrM & 2.50 & 1.69 & 1.91 & 0.12 & 6.23 & 40.80 & 3.43 & 2.23 & 2.48 & 0.14 & 8.80 & 113.29 \\ \(CM_{19}\) & 2.61 & 1.90 & 2.09 & 0.61 & 5.79 & 20.87 & 3.41 & 2.45 & 2.70 & 0.75 & 7.73 & 124.29 \\ \(CM_{34}\) & 2.86 & 2.23 & 2.38 & 0.66 & 6.13 & 20.67 & 3.91 & 2.97 & 3.17 & 0.82 & 8.69 & 102.71 \\ CDF & 45.00 & 4.96 & 9.27 & 0.26 & 167.64 & 179.54 & 45.42 & 6.33 & 11.33 & 0.32 & 166.02 & 179.40 \\ IIF & 2.88 & 2.06 & 2.22 & 0.60 & 6.61 & 22.85 & 3.93 & 2.58 & 2.86 & 0.77 & 9.41 & 49.67 \\ RGBuv & 2.34 & 1.70 & 1.81 & 0.51 & 5.33 & 21.96 & 3.12 & 2.21 & 2.38 & 0.63 & 7.22 & 34.57 \\ ChengSF & 2.90 & 1.97 & 2.21 & 0.54 & 6.80 & 34.91 & 4.02 & 2.55 & 2.90 & 0.68 & 9.87 & 123.33 \\ RGBuv+ChengSF & 2.22 & 1.60 & 1.74 & 0.45 & 5.06 & 18.22 & 2.93 & 2.04 & 2.26 & 0.57 & 6.77 & 21.28 \\ RGBuv+ChengSF+IIF & 2.13 & 1.55 & 1.69 & 0.48 & 4.76 & **14.83** & 2.80 & 2.06 & 2.23 & 0.59 & 6.35 & **18.79** \\ RGBuv+ChengSF+IIF+\(CM_{34}\) & 2.10 & 1.52 & 1.67 & 0.46 & **4.74** & 16.91 & 2.77 & 1.99 & 2.18 & 0.58 & **6.28** & 18.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative performance metrics between the proposed method and some conventional methods, with training and testing on the combination of the Gehler-Shi and Cube+ datasets. Figure 2: The recovery and reproduction AEs’ distributions for some conventional methods versus the proposed method with the combined features. In the two subfigures (**a,b**), the images of No. 1–50 refer to the first 50 images in the Gehler-Shi dataset, and the images of No. 51–200 refer to the first 150 images the Cube+ dataset. Figure 3: Comparison among the proposed, Luo’s [19], and conventional methods. The test images are from the Gehler-Shi and Cube+ datasets, and the resulting images are shown here by column corresponding to: (**1**) input images; (**2**) ground truths; (**3**) the proposed; (**4**) Luo’s, and (**5**) conventional methods (GW, WP, GE1, GE2, SOG, PCA-based, GGW, or LSR), respectively. These images include some common lighting scenes such as outdoor natural, outdoor artificial lighting, indoor artificial lighting, indoor natural lighting, combined natural and artificial lighting, etc. For each corrected image, the recovery AE (AE, for short) and the reproduction AE (RPE, for short) are displayed at the top, respectively. It should be noticed that these images displayed here are rendered just with some preliminary post-processing in the sRGB color space.