# Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques
## 9 Conclusions and Future Work
However, once the technology has been fully developed, it could easily be expanded to be applied in different and promising domains, such as building plans, electrical circuitry and so on. We plan to develop future versions of the tool that will implement the following features:  
* Classify a set of images, instead of one-by-one images. * Classify other kinds of diagrams (use case diagrams, state transition diagrams and so on). * Desktop version with the capability to explore private repositories. * Use the output of a generic search engine (such as Google) as an input to the tool, so that the tool is used to further filter the output of the generic search engine and improve its precision. * Integrate the image classification tool in a specific UML-oriented search engine, including a web crawler that indexes UML diagrams, with the potential to improve not only precision, but also recall. * Extract textual information from the diagram with the aid of OCR technologies. Combine with the previous feature to search the web for specific diagrams containing a given text. * Interpret the diagram according to the UML metamodel, producing its output as a model in XMI format. * Generate a graphical UML representation of the extracted model. All these ongoing developments, each one with different degree of completion, form part of a larger UML-oriented information retrieval project. Conceptualization, V.M. and G.G. ; methodology, V.M., G.G. and A.F. ; software, V.M. and M.A. ; validation, G.G. and A.F. ; formal analysis, V.M. and G.G. ; investigation, V.M., M.A. and A.F. ; data curation, V.M. and M.A. ; writing--original draft preparation, G.G. ; writing--review and editing, G.G. ; supervision, A.F., project administration, M.A. ; funding acquisition, V.M., M.A. and A.F. All authors have read and agreed to the published version of the manuscript. This research has received funding from the CRYSTAL project - Critical System Engineering Acceleration (European Union's Seventh Framework Program, FP7/2007-2013, ARTEMIS Joint Undertaking grant agreement n\({}^{\circ}\) 332830); and from the AMASS project - Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems (H2020-ECSEL grant agreement n\({}^{\circ}\) 692474; Spain's MINECO ref. PCIN-2015-262). We are grateful to the members of the Knowledge Reuse Group who acted as experts to classify the initial set of images downloaded from the web. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Precision for** \\ **Positives** \\ \end{tabular} & \begin{tabular}{c} **Recall for Positives** \\ **(Sensitivity)** \\ \end{tabular} & \
\begin{tabular}{c} **Precision for** \\ **Negatives** \\ \end{tabular} &
\begin{tabular}{c} **Recall for Negatives** \\ **(Specificity)** \\ \end{tabular} & **Accuracy** \\ \hline Support Vector Machine & 0.894 & 0.924 & 0.921 & 0.890 & 90.7\% \\ \hline
**Random Forest** & **0.909** & **0.959** & **0.957** & **0.904** & **93.2\%** \\ \hline
**J48 Decision Tree** & 0.903 & 0.925 & 0.923 & 0.901 & 91.3\% \\ \hline
**Logistic Regression** & **0.913** & **0.902** & **0.903** & **0.914** & **90.8\%** \\ \hline REP-Tree & 0.903 & 0.920 & 0.918 & 0.901 & 91.1\% \\ \hline Decision Table & 0.897 & 0.919 & 0.917 & 0.895 & 90.7\% \\ \hline
**This research** & **0.953** & **0.933** & **0.934** & **0.954** & **94.4\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the results of our method with those of [PERSON] et al. [25].