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Automatic classification of web images as UML static diagrams using machine learning techniques

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Knowledge Reusinges
dc.contributor.authorMoreno Pelayo, Valentín
dc.contributor.authorGénova Fuster, Gonzalo
dc.contributor.authorAlejandres Sánchez, Manuela
dc.contributor.authorFraga Vázquez, Anabel
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2022-02-21T10:24:07Z
dc.date.available2022-02-21T10:24:07Z
dc.date.issued2020-04-01
dc.description.abstractOur 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.en
dc.description.sponsorshipThis 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° 332830); and from the AMASS project – Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems (H2020-ECSEL grant agreement nº 692474; Spain’s MINECO ref. PCIN-2015-262).en
dc.identifier.bibliographicCitationMoreno, V.; Génova, G.; Alejandres, M.; Fraga, A. Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques. Appl. Sci. 2020, 10, 2406. https://doi.org/10.3390/app10072406en
dc.identifier.doihttps://doi.org/10.3390/app10072406
dc.identifier.issn2076-3417
dc.identifier.publicationfirstpage2406
dc.identifier.publicationissue7
dc.identifier.publicationtitleApplied Sciences (Switzerland)en
dc.identifier.publicationvolume10
dc.identifier.urihttps://hdl.handle.net/10016/34175
dc.identifier.uxxiAR/0000029188
dc.language.isoengen
dc.publisherMDPIen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/ART-010000-2013-1en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/692474en
dc.relation.projectIDGobierno de España. PCIN-2015-262
dc.rights© MDPI, 2020es
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subject.ecienciaInformáticaes
dc.subject.otheruml diagram recognitionen
dc.subject.otherimage processingen
dc.subject.otherimage classificationen
dc.subject.otherrule inductionen
dc.subject.otherclassification toolen
dc.titleAutomatic classification of web images as UML static diagrams using machine learning techniquesen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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