# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 4 Experimental Results
### Experiment B: Testing on Image Set from Identical Camera (Within Camera)  
This experiment uses some images from an identical camera as the training and testing data, which is to verify the estimation accuracy within the camera using the proposed method. Tables 3 and 4 compare the performance metrics for some conventional methods and our method with different features within an identical camera. Here Canon 5D is one \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 WP [6] & 9.69 & 7.48 & 8.56 & 1.72 & 20.49 & - & - & - & - & - & - & - \\ GW [4,5] & 7.71 & 4.29 & 4.98 & 1.01 & 20.19 & - & - & - & - & - & - & - \\ Gray pixels [42] & 6.65 & 3.26 & 3.95 & 0.68 & 18.75 & - & - & - & - & - & - & - \\ Color Tiger [52] & 3.91 & 2.05 & 2.53 & 0.98 & 10.00 & - & - & - & - & - & - & - \\ SoG [7] & 2.59 & 1.73 & 1.93 & 0.46 & 6.19 & - & - & - & - & - & - & - \\ GE1 [8] & 2.50 & 1.59 & 1.78 & 0.48 & 6.08 & - & - & - & - & - & - & - \\ GE2 [8] & 2.41 & 1.52 & 1.72 & 0.45 & 5.89 & - & - & - & - & - & - & - \\ GGW [8] & 2.38 & 1.43 & 1.66 & 0.35 & 6.01 & - & - & - & - & - & - & - \\ Attention CNN [56] & 2.05 & 1.32 & 1.53 & 0.42 & 4.84 & - & - & - & - & - & - & - \\ Lighting classification DL [15] & 1.86 & 1.27 & 1.39 & 0.42 & 4.31 & - & - & - & - & - & - & - \\ Luo [19] & 1.69 & 1.12 & 1.24 & 0.31 & 4.06 & - & - & - & - & - & - & - \\ Color Beaver (GW) [57] & **1.49** & **0.77** & **0.98** & **0.21** & **3.94** & - & - & - & - & - & - & - \\ \hline \multicolumn{13}{c}{**Proposed**} \\ \hline ClrM & 2.45 & 1.76 & 1.95 & 0.47 & 5.53 & 143.54 & 3.36 & 2.39 & 2.61 & 0.61 & 7.73 & 76.69 \\ \(CM_{19}\) & 2.16 & 1.52 & 1.71 & 0.41 & 4.97 & 20.51 & 2.88 & 1.96 & 2.26 & 0.52 & 6.69 & 25.38 \\ \(CM_{34}\) & 2.36 & 1.73 & 1.92 & 0.47 & 5.23 & 18.19 & 3.36 & 2.34 & 2.60 & 0.61 & 7.84 & 121.57 \\ IIF & 2.28 & 1.58 & 1.73 & 0.46 & 5.32 & 21.22 & 3.31 & 2.13 & 2.37 & 0.58 & 8.17 & 113.33 \\ RGBuv & 1.95 & 1.39 & 1.51 & 0.35 & 4.48 & 15.72 & 2.63 & 1.86 & 2.02 & **0.44** & 6.15 & 22.12 \\ ChengSF & 2.24 & 1.49 & 1.68 & 0.41 & 5.36 & 33.70 & 3.08 & 1.97 & 2.25 & 0.51 & 7.49 & 42.90 \\ RGBuv+ChengSF & 1.92 & 1.37 & 1.50 & 0.38 & 4.37 & 15.02 & 2.58 & 1.85 & 2.03 & 0.47 & 6.01 & **19.88** \\ RGBuv+ChengSF+IIF & 1.79 & 1.31 & 1.41 & 0.36 & 4.06 & **14.22** & 2.43 & 1.74 & 1.90 & **0.44** & **5.62** & 20.04 \\ RGBuv+ChengSF+IIF+\(CM_{34}\) & 1.80 & 1.31 & 1.42 & 0.36 & 4.08 & 14.33 & **2.44** & **1.73** & **1.89** & 0.45 & 5.64 & 20.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative performance metrics between our method and the previous methods on the Cube+ dataset. The statistical metrics of the previous methods are selected from [15,19]. of the cameras used by the Gehler-Shi dataset, and Canon EOS 550D used by the Cube+ dataset, respectively. For Canon 5D, 482 images are picked out from the Gehler-Shi dataset, and for Canon EOS 550D, 853 images are randomly selected from the Cube+ dataset. The standard 3-fold cross validation is independently applied to the constructed datasets to train and test the proposed method. Tables 3 and 4 show that the proposed method using image feature regressions achieves better performances than some traditional methods such as WP, GW, PCA-based, LSR, etc. Using the individual feature in the proposed method, the feature of RGBuv receives the best results. According to the discussion in the paper [49], the RGBuv feature can effectively cover the color distribution of an image. The results in Tables 3 and 4 also validate this viewpoint. It can be seen that compositing several features will provide better results, e.g., RGBuv+ChengSF, RGBuv+ChengSF+IIF, and RGBuv+ChengSF+IIF+\(CM_{34}\).