## 2 Literature Review
### Research Outcome  
The documents selected for this research came from various sources such as articles, newspaper reports, and books, but most came from conference papers. Furthermore, the areas associated with each article present a wide variety of results, with computer science presenting the highest percentage of articles, as shown in Table 1.  
Figure 4 displays a bar plot with the number of articles per country. It is possible to observe that the United States is the country that has contributed the most to the advancement of systems in this field. It is mainly related to the segmentation of graffiti for a better understanding of some gang' behaviors, locations, roots, and ambitions, since the number of gang-related crimes has increased in the US [21]. None of the selected articles were written by Portuguese authors.  
Although the detection and classification of graffiti is a topic that can help not only to minimize the damage related to illegal graffiti but also to spread the benefits of urban art, the literature review allowed us to realize that this subject has not yet been sufficiently explored in terms of technology. Of the selected articles, only 15 articles are related to graffiti identification or classification methods.  
From the reviewed articles, the most frequent target is the detection of illegal graffiti, as can be observed in Table 2. This can be justified by the importance of the negative connotation associated with legal graffiti, which brings discontent from the population and associated costs [8; 9; 10; 11]. As stated by [PERSON] et al., \"for many people, graffiti's presence suggests the government's failure to protect citizens and control lawbreakers\" [8], p. 1, and,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Subject Area** & **Percentage of Articles** \\ \hline Computer Science & 27.5\% \\ Social Sciences & 17.5\% \\ Engineering & 12.5\% \\ Decision Sciences & 12.5\% \\ Arts and Humanities (and News) & 12.5\% \\ Mathematics & 10\% \\ Physics and Astronomy & 7.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percentage of selected articles by field.  
Figure 4: Number of documents by country.  
for this reason, the need to find alternatives to control and minimize the costs related to the issue has become more and more certain.  
In addition, the segmentation of graffiti based on their similarities for the purpose of detecting gangs has also shown to be very valuable in this area because it helps law enforcement agencies understand the activities and territories of each gang [18,21].  
Of the selected articles, only two focus on classifying street art graffiti (only classifying as street art or no graffiti), and only one report [3] covers the detection of both types of graffiti without distinguishing them.  
Table 2 shows the number of articles based on each detection type.  
Regarding graffiti detection (illegal or art), the two most studied and developed methods were image processing, where detection was based on videos from surveillance cameras, and neural networks. The CNN (convolutional neural network) architecture appears to be the most thorough in this area, since it has been proven to be the best algorithm for image understanding and to provide very successful results in segmentation, classification, tagging, detection, and retrieval tasks [22,23].  
A total of 83% of the cases that used neural networks for detection were through transfer learning. Transfer learning is used to enhance the machine learning process of a domain by transferring information from a related problem instead of starting and learning from scratch [24].