# CNN-Based Illumination Estimation with Semantic Information
## 3 Experimental Results and Evaluations
This dataset includes both single- and multiple-illuminant natural images. The proposed network surpasses all its conventional counterparts in trimean and worst 25%. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **Mean** & **Median** & **Trimean** & **Best 25\%** & **Worst 25\%** \\ \hline \multicolumn{6}{c}{Statistics-Based Methods} \\ \hline White patch [3] & 7.55 & 5.68 & 6.35 & 1.42 & 16.12 \\ \hline Gray-world [32] & 6.36 & 6.28 & 6.28 & 2.33 & 10.58 \\ \hline
1 st-order grey edge [6] & 5.33 & 4.52 & 4.73 & 1.86 & 10.03 \\ \hline
2 nd-order grey edge [6] & 5.13 & 4.44 & 4.62 & 2.11 & 9.26 \\ \hline Shades of grey [4] & 4.93 & 4.01 & 4.23 & 1.14 & 10.20 \\ \hline General grey world [6] & 4.66 & 3.48 & 3.81 & 1.00 & 10.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative statistical metrics between the proposed network and conventional methods with [PERSON]’s re-processed dataset and the NUS-8 Camera Dataset (the lower, the better). Figure 5: [PERSON]’s re-processed dataset and their resulting images: (**a**) original image, (**b**) illumination estimation map, (**c**) weighted map, (**d**) image × weighted map, and (**e**) corrected image. Figure 6 is an angular error (AE) histogram comparison between the proposed network and several best-performing conventional methods selected from Table 1: CNN-based method, CCATI, ExemplarCC, ensemble of decision (ED) tree based method, [PERSON] et al., DS-Net, SqueezeNet-FC, and AlexNet-FC. [PERSON] and [PERSON] [40] proposed an exemplar method which estimates the local source illuminant by finding the neighboring surfaces in the training data which consists of the weak color constant RGB values and the texture features. Ensemble of decision tree based method [38] is a discrete version of Gamut mapping which uses the correlation matrix, instead of the canonical Gamut for the considered illuminants, and uses the image data to calculate the probability that the illumination in the test image is caused by which of the known illuminants. [PERSON] et al. [39] proposed a branch-level ensemble of neural networks consisting of two interacting sub-networks: a hypotheses network and a selection network. The selection network picks confident estimations from the plausible illuminant estimations generated from the hypotheses network. [PERSON]'s method produces accurate results, but the model size is huge, and its processing speed is slow. That is, when the CNNs go deeper by adding layers, fully connected layers have several well-known vital problems including incredibly expensive computational problems. To solve these problems, the proposed CNN method adopts the residual network to improve the estimation accuracy and reduce expensive computational cost. Further the pooling mechanism employed by the proposed network contributes to reducing estimation ambiguities. In estimating illuminants, the proposed network stays ahead of the state-of-the-art methods, with 76.41% of the tested images under an angular error of 3\({}^{\circ}\) and 97% under an angular error of 6\({}^{\circ}\). Figure 7 is a comparison of the root mean square error (RMSE) results among CNN-based, exemplar-based, ensemble of decision (ED) tree based methods, Alex-FC, CCATI, Zhan et al., DS-Net, SqueezeNet-FC, and the proposed network (proposed), with the input of the angular error. The proposed network records the lower RMSE relative to its conventional counterparts. Therefore, the proposed network is deemed to be robust and generates lower AE and RMSE in estimating illumination of a wide range of image scenes. To further verify the proposed method, additional experiments were conducted using SFU-lab dataset [33] and gray-ball dataset [43].