UML Class Model Generation of Images Using Neural Networks

This paper presents the way machine learning and deep learning techniques are used to classify boats and extract their attributes in a UML model. This is based on the development and the implementation of a classification and feature extraction application for boat types. The case study aims to expl...

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Bibliographic Details
Published in2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP) Vol. CFP2255E-ART; pp. 1 - 4
Main Authors Nedelcu, Irina-Gabriela, Ionita, Anca Daniela, Mocanu, Stefan Alexandru, Saru, Daniela
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2022
Subjects
artificial intelligence
classification
Computational modeling
Deep learning
feature extraction
Machine learning algorithms
Neural networks
Object oriented modeling
Training
UML
Unified modeling language
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Summary:This paper presents the way machine learning and deep learning techniques are used to classify boats and extract their attributes in a UML model. This is based on the development and the implementation of a classification and feature extraction application for boat types. The case study aims to explore the bridge between artificial intelligence algorithms for computer vision and the domain of object-oriented modeling. The paper describes methods applied to process the dataset and their labels to train a neural network model. The resulting model can classify boat types and provide their attributes, based on what can be identified in the image and output the textual result in a UML class diagram format.
ISSN:2157-8702
DOI:10.1109/IWSSIP55020.2022.9854486