Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques
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 communicat...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-04-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/10/7/2406 |
id |
doaj-95b3118cfc254558b626089e894be886 |
---|---|
record_format |
Article |
spelling |
doaj-95b3118cfc254558b626089e894be8862020-11-25T02:04:11ZengMDPI AGApplied Sciences2076-34172020-04-01102406240610.3390/app10072406Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning TechniquesValentín Moreno0Gonzalo Génova1Manuela Alejandres2Anabel Fraga3Knowledge Reuse Group, Departamento de Informática, Universidad Carlos III de Madrid. Av. Universidad 30, 28911 Leganés (Madrid), SpainKnowledge Reuse Group, Departamento de Informática, Universidad Carlos III de Madrid. Av. Universidad 30, 28911 Leganés (Madrid), SpainKnowledge Reuse Group, Departamento de Informática, Universidad Carlos III de Madrid. Av. Universidad 30, 28911 Leganés (Madrid), SpainKnowledge Reuse Group, Departamento de Informática, Universidad Carlos III de Madrid. Av. Universidad 30, 28911 Leganés (Madrid), SpainOur 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.https://www.mdpi.com/2076-3417/10/7/2406UML diagram recognitionimage processingimage classificationrule inductionclassification tool |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Valentín Moreno Gonzalo Génova Manuela Alejandres Anabel Fraga |
spellingShingle |
Valentín Moreno Gonzalo Génova Manuela Alejandres Anabel Fraga Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques Applied Sciences UML diagram recognition image processing image classification rule induction classification tool |
author_facet |
Valentín Moreno Gonzalo Génova Manuela Alejandres Anabel Fraga |
author_sort |
Valentín Moreno |
title |
Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques |
title_short |
Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques |
title_full |
Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques |
title_fullStr |
Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques |
title_full_unstemmed |
Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques |
title_sort |
automatic classification of web images as uml static diagrams using machine learning techniques |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-04-01 |
description |
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. |
topic |
UML diagram recognition image processing image classification rule induction classification tool |
url |
https://www.mdpi.com/2076-3417/10/7/2406 |
work_keys_str_mv |
AT valentinmoreno automaticclassificationofwebimagesasumlstaticdiagramsusingmachinelearningtechniques AT gonzalogenova automaticclassificationofwebimagesasumlstaticdiagramsusingmachinelearningtechniques AT manuelaalejandres automaticclassificationofwebimagesasumlstaticdiagramsusingmachinelearningtechniques AT anabelfraga automaticclassificationofwebimagesasumlstaticdiagramsusingmachinelearningtechniques |
_version_ |
1724944034244329472 |