## 2 Literature Review
### Related Work
This algorithm allowed the production of a density map containing the graffiti artworks found in the central part of London. [PERSON] et al. [15] try to investigate how a CNN model performs in the detection of art graffiti. The main contribution of this paper is to demonstrate that, when using transfer learning, instead of removing the last fully connected layer for a layer containing the desired number of final classes in the neural networks, it is more efficient to maintain it. [PERSON] et al. argue that removing the last layer may result in a loss of relevant information for the new task, and, with this approach, they were able to achieve the results faster, with a smaller number of epochs. Besides the previously mentioned works, there was one article whose goal was the detection of any type of graffiti, whether it was considered art or not, such as the approach applied to Medellin City [3] that used the PyTorch library to implement an R-CNN, the ResNet50 classifier, already pre-trained on the ImageNet dataset. This research allowed the construction of a visualization tool through heat maps that, besides helping define measures to improve sectorial policies, also allow better control and definition of efforts to preserve the areas rich in art graffiti and restore those with a negative aspect. As future work, the paper mentions a possible improvement to a more in-depth graffiti classification based on their form or purpose. In addition to graffiti detection, a new topic is being increasingly discussed with graffiti data: gang identification by segmenting the graffiti based on their similarities. The analysis and interpretation of gang graffiti can help law enforcement better understand their activities and where they need to operate to respond and have an idea of the gang's intentions [16; 17; 18; 19]. The system implemented by [PERSON] et al. [17] assumes that two graffiti are more likely to be created by the same graffiti artist if they have high similarities in visual and contextual aspects. This system starts by extracting visual features (such as letters, numbers, and symbols) through OCR (Optical Character Recognition) and selecting the most similar images. Then, the similarities between the images are calculated, and, this way, it is possible to identify the most similar photos, which will correspond to those with a higher probability of having been drawn by the same individuals. The results obtained achieved an accuracy of about 64%. Graffiti-ID [20] was a research project conducted at Michigan State University that aimed to return similar graffiti from their database. For this, the Scale Invariant Feature Transform (SIFT) was used to extract the most relevant visual features (which are referred to as critical points). Then, the graffiti association was based on calculating the similarities via Euclidean distances between the critical points of the two images. Local geometric constraints were added to try to reduce false associations. A CNN model was adopted by [PERSON] et al. [16] to classify graffiti into different classes based on a set of graffiti components. The model was composed of five convolutional layers followed by three fully connected ones and a final softmax layer that achieved an overall accuracy of 87%. [PERSON] et al. [21] proposed a model-based system that, by analyzing graffiti images, can present relevant information about the gangs associated with the graffiti. This system is composed of three methods: color recognition (taking advantage of the capabilities of the modern mobile device's touch screen and the user's help to trace the path along the color region), segmentation of the color image based on Gaussian thresholding, and content-based retrieval of the graffiti to detect the graffiti and identify its components as objects and shapes (such as stars, pitchforks, crowns, and arrows). From here, a hierarchical k-means clustering is used to create vocabulary trees.