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Our purpose in this research is to develop a method to automatically and efficiently classify web images as Unified Modeling Language (UML) static diagrams, and to produce a computer tool that implements this function. The tool receives a bitmap file (in different formats) as an input and communicates whether the image corresponds to a diagram. For pragmatic reasons, we restricted ourselves to the simplest kinds of diagrams that are more useful for automated software reuse: computer-edited 2D representations of static diagrams. The tool does not require that the images are explicitly or implicitly tagged as UML diagrams. The tool extracts graphical characteristics from each image (such as grayscale histogram, color histogram and elementary geometric forms) and uses a combination of rules to classify it. The rules are obtained with machine learning techniques (rule induction) from a sample of 19,000 web images manually classified by experts. In this work, we do not consider the textual contents of the images. Our tool reaches nearly 95% of agreement with manually classified instances, improving the effectiveness of related research works. Moreover, using a training dataset 15 times bigger, the time required to process each image and extract its graphical features (0.680 s) is seven times lower.

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Context: It is already common to compute quantitative metrics of requirements to assess their quality. However, the risk is to build assessment methods and tools that are both arbitrary and rigid in the parameterization and combination of metrics. Specifically, we show that a linear combination of metrics is insufficient to adequately compute a global measure of quality.Objective: In this work we propose to develop a flexible method to assess and improve the quality of requirements that can be adapted to different contexts, projects, organizations and quality standards, with a high degree of automation.Based Systems Engineering, member of INCOSE (International Council on Systems Engineering) and AFIS (Association Française d'Ingénierie Système).

In the context of digitization of photographs and other documents with graphical value, cultural heritage organizations need to give a guarantee that the stored digital image is a faithful representation of the physical image both at the physical level and the perceptual level. On the physical level, image quality can be measured objectively in a simple way by applying certain physical attributes to the image, as well as by measuring how distorting images affects the performance of the attributes. However, on the perceptual level, image quality should correspond to the perception that a human expert would experience when observing the physical image under certain determined and controlled conditions. In this paper we address the problem of image quality assessment (IQA) in the context of cultural heritage digitization by applying machine learning (ML). In particular, we explore the possibility of creating a decision tree that mimics the response of an expert on cultural heritage when observing cultural heritage images.