# Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques
## 3 Related Work  
As we have argued, the existence of large public model repositories would greatly ease model reuse. However, to this day, there are few such repositories available [10]. Some tool vendors (such as Sparx Enterprise Architect and Visual Paradigm) offer their own private repositories on a commercial basis, with two main drawbacks: these repositories only support the native file format of their own CASE tools, and there is no open access for models created by others tools. On the public side, a pioneering repository for model-driven development, called ReMoDD, was proposed and developed by [PERSON] et al. [11; 12]. However, those models are stored as files that have to be downloaded and opened with a compatible CASE tool, or else they are pure PDF files. Consequently, those models are not easily retrievable; users have to search manually for UML diagrams among hundreds of files, with limited search capabilities. In general, there are few examples of good online model repositories that people can consult, and efforts toward repositories of such models have achieved limited success [13]. Therefore, even though several initiatives have been promoted to support the adoption of model repositories in MDE, we are still far from a concrete adoption of them [14]. Keyword-based and content-based retrieval of models from repositories was studied by [PERSON] et al. [15; 16]. This is a good testimony of the importance of repositories and retrieval techniques in model-driven engineering. However, this same research is focused only on textual elements of models, i.e., it requires that models are stored in various textual formats, basically XML or its derivations (such as XMI). They do not consider the interpretation of models stored as images. A recent and extensive study of models stored in various formats in GitHub, combining automatic processing and a lot of manual work, identified 93,596 UML models from 24,717 different repositories, of which 57,822 (61.8%) are images, the rest being files with extensions.xmi or.uml [17]. This confirms the existence of potentially useful and interesting information in repositories that is still difficult to access and reuse; and it also confirms that a large proportion of models is stored as images. The interest in automatic UML diagram recognition in repositories goes back to the times of appearance of the UML, with an initial emphasis on sketch recognition early in the software design cycle, in order to make the information they contain available to CASE tools [18]. The need of the MDE/MDD community to manage large repositories supporting the effective reuse of modeling artifacts has increased the need for the recognition of tool-made diagrams [14]. However, to our knowledge, the research in this field has been rather scarce up to now, even less using machine learning techniques. Most of the approaches use image processing and pattern recognition methods adapted to particular domains, such as architectural diagrams [19] or finite state automata diagrams [20]. In the domain of UML diagrams, [PERSON] and [PERSON] have published research on the same problem [21], although they do not use an automatic learning approach, but a fixed set of classification criteria. Their Img2 UML tool [22] uses the Aforge.NET framework (an open source C# framework designed for developers and researchers in the fields of computer vision and artificial intelligence, including image processing, neural networks, genetic algorithms, fuzzy logic, machine learning, robotics, etc.). The tool extracts UML class models from pixmap images and exports them into XMI files that can be read by a commercial CASE tool (StarUML). They created a private repository with images collected from the internet together with their corresponding XMI extraction [23]. In this sense, their work covers nearly all the steps we envision for future versions of our tool.