## 2 Materials and Methods
### Data Collection and Pre-Processing
#### 2.1.2 Data Pre-Processing  
The data pre-processing step is very critical for our approach. For this phase, we discarded the non-relevant samples in the aforementioned datasets. Unfortunately, the required labels for the data we collected were incomplete. We found that paintings were the most complete and fully annotated category, especially the samples collected from Wikiart.org. The most relevant labels we selected for the paintings were the artist name, the year of creation, the genre, the style and the medium. We selected a total of 43,594 paintings annotated for all of these labels. To fully evaluate the performance of our approach, we created four datasets regarding the number of paintings per artist, as we found that the artist attribution is the most relevant task for painting categorization (see Table 1). Each of the new sub-datasets retains only paintings of the artists that have a higher artwork count than the dataset threshold. For example, the \(>\)50 dataset will only host paintings by artists that painted more than 50 paintings. This was intended to evaluate the impact of the number of paintings per artist on the results, as often artists do not change style or genre, and their paintings are only created within a specific time frame. This can help the model in learning correlations between labels. The five textual labels were encoded using One Hot Encoding and the year label was divided into bins of 10 years. Each entry in the datasets consists of a painting identified by its content ID, which points to the image file and its five annotations. At the training stage, the media, the style and the genre are fed to the model along with the asset image. The artist and the year are the output of the model. Table 2 represents a dataset sample of five entries. At the training stage, for each item in the dataset, the data are split into two sets: The input set X, which consists of the visual capture, the genre, the style and the medium; and the output set Y, which consists of the artist name and the creation year. For visual data, we chose to resize the images to a minimum of 300 pixels either in length or width (preserving the aspect ratio). This was mainly done to increase the performance of the online data augmentation while training the models. The main purpose of data augmentation is to increase \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Samples Per Artist** & **Data Samples Included** & **Number of Artists Involved** \\ \hline all & 43,594 & 917 \\ \(>\)50 & 34,759 & 227 \\ \(>\)100 & 26,141 & 107 \\ \(>\)300 & 11,166 & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selection of the painting datasets used in this study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Content ID** & **Artist** & **Creation Year** & **Media** & **Genre** & **Style** \\ \hline
323653 & [PERSON] & 1874 & Oil, Canvas & Portrait & Realism \\
452365 & [PERSON] & 1858 & Oil, Canvas & Landscape & Luminism \\
179324 & [PERSON] & 1944 & Oil, Canvas & Still life & Surrealism \\
197632 & [PERSON] & 1897 & Pasted & Landscape & Impressionism \\
127801 & [PERSON] & 1504 & Oil, Panel & Portrait & High Renaissance \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset sample. Figure 3: Some artworks from the Rijksmuseum dataset. the validation and generalization performance of the trained CNN by avoiding the phenomenon of overfitting, where the network becomes unable to accurately classify samples not used in the training phase. The data augmentation generally extends the training dataset with an augmented dataset composed of the same images with distortions and adjustments such as flips, zoom, rotations, whitening, contrast and lighting adjustments, etc. This new augmented set will help the network avoid overfitting by training the network with slightly modified samples at each iteration. In our experiments, we initially tested data augmentation with the single input network to evaluate its impact (comparing the training of normal images with augmented images).