# Classification of Architectural Heritage Images Using Deep Learning Techniques
## 3 Results
### Comparison of the Results
#### 3.3.1 Failures Detected Using CNNs in Image Classification  
To improve the results of the network, it is important to understand what the failures are and to detect their possible origins to properly address the problems to be solved. The two most common sources of error that have been found are: the presence of other elements in the images and that the element of the image to be classified is similar to another element. Regarding the first problem, it is clear that this type of error is difficult to correct, since it is often unavoidable for several elements to be classified in the image. The best solution may be to use the two or three most likely classifications offered by the network and not only the most likely, as discussed in the preceding paragraph. This will enrich the classification achieved, although at the cost of greater complexity in managing the results. As for the cases in which the network confuses an element with a similar one, the best solution is usually to add more training images that make it easier to distinguish some elements from others more efficiently.  
Table 12 shows several significant examples of the most common errors, already mentioned, that have been found in the image classification results using convolutional neural networks. There is sometimes an ambiguity in the main element that the image wants to represent, so it is not easy to solve this issue (beyond offering several probable elements, as mentioned above). Another additional issue is that, in some images, elements appear that have not been specifically trained (such as the case of a capital that is confused with a gargoyle or a rose window that can be confused with the interior of a dome); these types of errors could probably be solved by introducing new categories to consider these new elements. Finally, sometimes, the element to be classified is hardly observable in the images because of its small size or a lack of contrast with the background.  
\begin{table}
\begin{tabular}{c c c c c} \hline Bell tower: 78.22\% & Bell tower: 76.94\% & Gargoyle: 57.11\% & Dome (inner): 70.72\% & Apse: 63.61\% \\ Dome (outer): 19.39\% & Dome (outer): 21.99\% & Column: 33.95\% & Vault: 27.02\% & Column: 36.15\% \\ Apse: 2.36\% & Gargoyle: 0.71\% & Flying buttress: 6.83\% & Stained glass: 2.24\% & Bell tower: 0.21\% \\ \hline \end{tabular}
\end{table}
Table 11: Examples of best predictions using the ResNet network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Correct Category** & **Images Incorrectly Classified (and the Corresponding Wrong Categories)** \\ \hline Altar & & & \\  & Column & Stained glass & Vault \\ \hline Apse & & & \\  & Dome (outer) & Flying buttress & Altar & Bell tower \\ \hline Bell tower & & & \\  & Gargoyle & Column & Apse & Dome(outer) \\ \hline Column & & & \\  & Altar & Flying buttress & Gargoyle & Stained glass \\ \hline Dome (inner) & & & \\  & Vault & Altar & \\ \hline Dome (outer) & & & \\  & Bell tower & Gargoyle & Altar \\ \hline Flying buttress & & & \\  & Bell tower & Gargoyle & Altar \\ \hline Gargoyle & & & \\  & Flying buttress & Column & Bell tower \\ \hline Stained glass & & & \\  & Altar & Column \\ \hline Vault & & & \\  & Altar & Apse \\ \hline \hline \end{tabular}
\end{table}
Table 12: Examples of images incorrectly classified by the convolutional neural networks used.