# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 4 Experimental Results
### Experiment A: Individually Testing on Different Datasets (Within Dataset)  
In this experiment, our method is independently applied to different datasets. Tables 1 and 2 compare some performance metrics between our method and some conventional algorithms on the Gehler-Shi dataset and the Cube+ dataset, respectively. In order to illustrate the impacts of different features on illuminant estimation performances, all types of features given in Section 3.1 are investigated using the proposed method. \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] & 7.55 & 5.68 & 6.35 & 1.45 & 16.12 & - & 8.1 & 6.5 & 7.1 & - & - & - \\ Edge-based Gamut [28] & 6.52 & 5.04 & 5.43 & 1.90 & 13.58 & - & - & - & - & - & - & - \\ GW [4; 5] & 6.36 & 6.28 & 6.28 & 2.33 & 10.58 & - & 7.0 & 6.8 & 6.9 & - & - & - \\ GE1 [8] & 5.33 & 4.52 & 4.73 & 1.86 & 10.03 & - & 6.4 & 4.9 & 5.3 & - & - & - \\ GE2 [8] & 5.13 & 4.44 & 4.62 & 2.11 & 9.26 & - & 6.0 & 4.8 & 5.2 & - & - & - \\ SoG [7] & 4.93 & 4.01 & 4.23 & 1.14 & 10.20 & - & 5.8 & 4.4 & 4.9 & - & - & - \\ Bayesian [29] & 4.82 & 3.46 & 3.88 & 1.26 & 10.49 & - & 5.6 & 3.9 & 4.4 & - & - & - \\ GGW [8] & 4.66 & 3.48 & 3.81 & 1.00 & 10.09 & - & 5.3 & 4.0 & 4.4 & - & - & - \\ Natural Image Statistics [9] & 4.19 & 3.13 & 3.45 & 1.00 & 9.22 & - & 4.8 & 3.5 & 3.9 & - & - & - \\ CART-based Combination [17] & 3.9 & 2.9 & 3.3 & - & - & - & 4.5 & 3.5 & 3.8 & - & - & - \\ WCS with partitioning [40] & 3.66 & 2.05 & 2.45 & 0.44 & 9.49 & - & 4.83 & 2.52 & 3.24 & - & - & - \\ PCA-based [21] & 3.52 & 2.14 & 2.47 & 0.50 & 8.74 & - & 4.71 & 2.72 & 3.28 & - & - & - \\ LSR [44] & 3.31 & 2.80 & 2.87 & 1.14 & 6.39 & - & - & - & - & - & - & - \\ CNN-based method [46] & 2.63 & 1.98 & 2.10 & 0.72 & 3.90 & - & - & - & - & - & - & - \\ FFCC [32] & 1.61 & **0.86** & 1.02 & **0.23** & 4.27 & - & **2.12** & **1.07** & **1.35** & - & - & - \\ C4-SqueezeNet-FC4 [55] & **1.35** & 0.88 & **0.99** & 0.28 & **3.21** & - & - & - & - & - & - & - \\ \hline \multicolumn{13}{c}{**Proposed**} \\ \hline ClrM & 2.59 & 1.35 & 1.76 & 0.00 & 7.53 & 19.28 & 3.34 & 1.57 & 2.13 & 0.00 & 9.82 & 25.43 \\ \(CM_{19}\) & 3.33 & 2.62 & 2.76 & 0.69 & 7.26 & 19.94 & 4.29 & 3.30 & 3.50 & 0.85 & 9.53 & **23.42** \\ \(CM_{34}\) & 3.62 & 2.95 & 3.05 & 0.80 & 7.72 & 20.43 & 4.67 & 3.55 & 3.81 & 0.98 & 10.25 & 33.69 \\ CDF & 8.65 & 2.60 & 3.11 & 0.00 & 29.53 & 177.12 & 9.64 & 3.05 & 3.82 & 0.00 & 32.31 & 177.44 \\ IIF & 4.14 & 3.03 & 3.37 & 0.97 & 9.06 & 22.34 & 5.40 & 3.68 & 4.28 & 1.13 & 12.37 & 36.04 \\ RGBuv & 3.25 & 2.35 & 2.55 & 0.67 & 7.35 & 24.81 & 4.39 & 2.98 & 3.22 & 0.79 & 10.48 & 110.51 \\ ChengSF & 4.17 & 3.03 & 3.41 & 0.85 & 9.29 & 19.04 & 5.47 & 3.63 & 4.30 & 0.98 & 12.72 & 30.84 \\ RGBuv+ChengSF & 3.06 & 2.20 & 2.40 & 0.68 & 6.85 & **18.25** & 3.98 & 2.73 & 2.99 & 0.79 & 9.27 & 29.51 \\ RGBuv+ChengSF+IIF & 3.01 & 2.24 & 2.40 & 0.67 & 6.70 & 19.48 & 3.94 & 2.75 & 2.97 & 0.79 & 9.17 & 47.80 \\ RGBuv+ChengSF+IIF+\(CM_{34}\) & 2.99 & 2.17 & 2.36 & 0.68 & 6.68 & 19.16 & 3.90 & 2.80 & 3.02 & 0.80 & **9.12** & 45.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative performance metrics between our method and some statistics-/learning-based methods on the Gehler-Shi dataset. The statistical metrics of the conventional methods are cited from [40] and the related literature. Table 1 shows that our results (except for the one using CDF features) outperform most unitary algorithms on the Gehler-Shi dataset. For example, the Mean of recovery AE is 4.66 for GGW, and 4.17 for our method using ChengSF; The Trimean of recovery AE is 3.81 for GGW, and 3.41 for our method using ChengSF. There is a significant performance improvement near 10% from GGW to our method using ChengSF.