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A convolutional neural networks based approach for clustering of emotional elements in art design

The rapid advancement of industrialization has sparked the emergence of diverse art and design theories. As a trailblazer in the realm of industrial art and design theory, visual communication has transcended the boundaries of merely arranging and combining individual elements. Embracing the potenti...

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Detalles Bibliográficos
Autor principal: Rui, Xue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495934/
https://www.ncbi.nlm.nih.gov/pubmed/37705618
http://dx.doi.org/10.7717/peerj-cs.1548
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author Rui, Xue
author_facet Rui, Xue
author_sort Rui, Xue
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description The rapid advancement of industrialization has sparked the emergence of diverse art and design theories. As a trailblazer in the realm of industrial art and design theory, visual communication has transcended the boundaries of merely arranging and combining individual elements. Embracing the potential of artificial intelligence technology, the extraction of multidimensional abstract data and the acceleration of the art design process have gained considerable momentum. This study delves into the abstract emotional facets within the methodology of visual communication art design. Initially, convolutional neural networks (CNN) are employed to extract expressive features from the poster’s visual information. Subsequently, these features are utilized to cluster emotional elements using a variational autoencoder (VAE). Through this clustering process, the poster images are categorized into positive, negative, and neutral classes. Experimental results demonstrate a silhouette coefficient surpassing 0.7, while the system framework exhibits clustering accuracy and efficiency exceeding 80% in single sentiment class testing. These outcomes underscore the efficacy of the proposed CNN-VAE-based clustering framework in analyzing the dynamic content of design elements. This framework presents a novel approach for future art design within the realm of visual communication.
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spelling pubmed-104959342023-09-13 A convolutional neural networks based approach for clustering of emotional elements in art design Rui, Xue PeerJ Comput Sci Algorithms and Analysis of Algorithms The rapid advancement of industrialization has sparked the emergence of diverse art and design theories. As a trailblazer in the realm of industrial art and design theory, visual communication has transcended the boundaries of merely arranging and combining individual elements. Embracing the potential of artificial intelligence technology, the extraction of multidimensional abstract data and the acceleration of the art design process have gained considerable momentum. This study delves into the abstract emotional facets within the methodology of visual communication art design. Initially, convolutional neural networks (CNN) are employed to extract expressive features from the poster’s visual information. Subsequently, these features are utilized to cluster emotional elements using a variational autoencoder (VAE). Through this clustering process, the poster images are categorized into positive, negative, and neutral classes. Experimental results demonstrate a silhouette coefficient surpassing 0.7, while the system framework exhibits clustering accuracy and efficiency exceeding 80% in single sentiment class testing. These outcomes underscore the efficacy of the proposed CNN-VAE-based clustering framework in analyzing the dynamic content of design elements. This framework presents a novel approach for future art design within the realm of visual communication. PeerJ Inc. 2023-09-06 /pmc/articles/PMC10495934/ /pubmed/37705618 http://dx.doi.org/10.7717/peerj-cs.1548 Text en ©2023 Rui https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Rui, Xue
A convolutional neural networks based approach for clustering of emotional elements in art design
title A convolutional neural networks based approach for clustering of emotional elements in art design
title_full A convolutional neural networks based approach for clustering of emotional elements in art design
title_fullStr A convolutional neural networks based approach for clustering of emotional elements in art design
title_full_unstemmed A convolutional neural networks based approach for clustering of emotional elements in art design
title_short A convolutional neural networks based approach for clustering of emotional elements in art design
title_sort convolutional neural networks based approach for clustering of emotional elements in art design
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495934/
https://www.ncbi.nlm.nih.gov/pubmed/37705618
http://dx.doi.org/10.7717/peerj-cs.1548
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