Artificial Intelligence Perspectives on Mexican Art: A Case Study

Espinosa Zuniga Javier Jesus; Juarez Caballero Grelda Yazmin

doi:doi:10.11648/j.ajset.20230801.17

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Artificial Intelligence Perspectives on Mexican Art: A Case Study

Espinosa Zuniga Javier Jesus, Juarez Caballero Grelda Yazmin

Published in American Journal of Science, Engineering and Technology (Volume 8, Issue 1)

Received: 11 February 2023 Accepted: 27 February 2023 Published: 9 March 2023

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Abstract

In this article a set of images corresponding to paintings of eight painters considered an Artistic Heritage of Mexico was clustered to identify clusters of images with similar characteristics between themselves. The images were acquired from a public source available on the Internet, a Pre-processing phase was applied in order to standardize the images in size and number of pixels, an extraction phase of features was applied for each image using Principal Components Analysis (PCA) and Histograms of Oriented Gradients (HOG), a segmentation phase of the features that were derived in the extraction phase was applied using the K-Means technique and the quality of the clusters that were obtained was evaluated using the Silhouette measure. As a result, seven clusters were attained with interesting characteristics: two of the most renowned Mexican painters worldwide whose artistic work is known for using a rich variety of shapes and colors (Diego Rivera and Frida Kahlo) clearly predominated in two clusters; an artist who is recognized for capturing Mexican landscapes in his paintings (José María Velasco) predominated in another cluster; in other three clusters a mixture of various Mexican artists predominated and in the last cluster Diego Rivera clearly predominated. According to the results, it seems that the paintings of Diego Rivera stand out due to a greater number of shapes used compared to the rest of the paintings analyzed. This article is a sample of the potential of Artificial Intelligence applied to Mexican art (and to art in general).

Published in	American Journal of Science, Engineering and Technology (Volume 8, Issue 1)
DOI	10.11648/j.ajset.20230801.17
Page(s)	63-70
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Principal Components Analysis (PCA), Histograms of Oriented Gradients (HOG), Clustering, K-Means

References

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[4]	Jafarpour S., Polatkan G., Brevdo E., Hughes S., Brasoveanu A. y Daubechies, I. Stylistic Analysis of Paintings using Wavelets and Machine Learning. 2009 17th European Signal Processing Conference, 2009, pp. 1220-1224.
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[9]	Dudek, A. (2020). Silhouette Index as Clustering Evaluation Tool. In: Jajuga, K., Batóg, J., Walesiak, M. (eds) Classification and Data Analysis. SKAD 2019. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-52348-0_2
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Espinosa Zuniga Javier Jesus, Juarez Caballero Grelda Yazmin. (2023). Artificial Intelligence Perspectives on Mexican Art: A Case Study. American Journal of Science, Engineering and Technology, 8(1), 63-70. https://doi.org/10.11648/j.ajset.20230801.17

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Espinosa Zuniga Javier Jesus; Juarez Caballero Grelda Yazmin. Artificial Intelligence Perspectives on Mexican Art: A Case Study. Am. J. Sci. Eng. Technol. 2023, 8(1), 63-70. doi: 10.11648/j.ajset.20230801.17

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AMA Style

Espinosa Zuniga Javier Jesus, Juarez Caballero Grelda Yazmin. Artificial Intelligence Perspectives on Mexican Art: A Case Study. Am J Sci Eng Technol. 2023;8(1):63-70. doi: 10.11648/j.ajset.20230801.17

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@article{10.11648/j.ajset.20230801.17,
  author = {Espinosa Zuniga Javier Jesus and Juarez Caballero Grelda Yazmin},
  title = {Artificial Intelligence Perspectives on Mexican Art: A Case Study},
  journal = {American Journal of Science, Engineering and Technology},
  volume = {8},
  number = {1},
  pages = {63-70},
  doi = {10.11648/j.ajset.20230801.17},
  url = {https://doi.org/10.11648/j.ajset.20230801.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajset.20230801.17},
  abstract = {In this article a set of images corresponding to paintings of eight painters considered an Artistic Heritage of Mexico was clustered to identify clusters of images with similar characteristics between themselves. The images were acquired from a public source available on the Internet, a Pre-processing phase was applied in order to standardize the images in size and number of pixels, an extraction phase of features was applied for each image using Principal Components Analysis (PCA) and Histograms of Oriented Gradients (HOG), a segmentation phase of the features that were derived in the extraction phase was applied using the K-Means technique and the quality of the clusters that were obtained was evaluated using the Silhouette measure. As a result, seven clusters were attained with interesting characteristics: two of the most renowned Mexican painters worldwide whose artistic work is known for using a rich variety of shapes and colors (Diego Rivera and Frida Kahlo) clearly predominated in two clusters; an artist who is recognized for capturing Mexican landscapes in his paintings (José María Velasco) predominated in another cluster; in other three clusters a mixture of various Mexican artists predominated and in the last cluster Diego Rivera clearly predominated. According to the results, it seems that the paintings of Diego Rivera stand out due to a greater number of shapes used compared to the rest of the paintings analyzed. This article is a sample of the potential of Artificial Intelligence applied to Mexican art (and to art in general).},
 year = {2023}
}

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TY  - JOUR
T1  - Artificial Intelligence Perspectives on Mexican Art: A Case Study
AU  - Espinosa Zuniga Javier Jesus
AU  - Juarez Caballero Grelda Yazmin
Y1  - 2023/03/09
PY  - 2023
N1  - https://doi.org/10.11648/j.ajset.20230801.17
DO  - 10.11648/j.ajset.20230801.17
T2  - American Journal of Science, Engineering and Technology
JF  - American Journal of Science, Engineering and Technology
JO  - American Journal of Science, Engineering and Technology
SP  - 63
EP  - 70
PB  - Science Publishing Group
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UR  - https://doi.org/10.11648/j.ajset.20230801.17
AB  - In this article a set of images corresponding to paintings of eight painters considered an Artistic Heritage of Mexico was clustered to identify clusters of images with similar characteristics between themselves. The images were acquired from a public source available on the Internet, a Pre-processing phase was applied in order to standardize the images in size and number of pixels, an extraction phase of features was applied for each image using Principal Components Analysis (PCA) and Histograms of Oriented Gradients (HOG), a segmentation phase of the features that were derived in the extraction phase was applied using the K-Means technique and the quality of the clusters that were obtained was evaluated using the Silhouette measure. As a result, seven clusters were attained with interesting characteristics: two of the most renowned Mexican painters worldwide whose artistic work is known for using a rich variety of shapes and colors (Diego Rivera and Frida Kahlo) clearly predominated in two clusters; an artist who is recognized for capturing Mexican landscapes in his paintings (José María Velasco) predominated in another cluster; in other three clusters a mixture of various Mexican artists predominated and in the last cluster Diego Rivera clearly predominated. According to the results, it seems that the paintings of Diego Rivera stand out due to a greater number of shapes used compared to the rest of the paintings analyzed. This article is a sample of the potential of Artificial Intelligence applied to Mexican art (and to art in general).
VL  - 8
IS  - 1
ER  -

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Author Information

Espinosa Zuniga Javier Jesus

Department of Customer Relationship Management, Ve Por Mas Financial Group, Mexico City, Mexico
Juarez Caballero Grelda Yazmin

Department of Biostatistics, The University of Iowa, Iowa, USA

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APA Style

Espinosa Zuniga Javier Jesus, Juarez Caballero Grelda Yazmin. (2023). Artificial Intelligence Perspectives on Mexican Art: A Case Study. American Journal of Science, Engineering and Technology, 8(1), 63-70. https://doi.org/10.11648/j.ajset.20230801.17

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ACS Style

Espinosa Zuniga Javier Jesus; Juarez Caballero Grelda Yazmin. Artificial Intelligence Perspectives on Mexican Art: A Case Study. Am. J. Sci. Eng. Technol. 2023, 8(1), 63-70. doi: 10.11648/j.ajset.20230801.17

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AMA Style

Espinosa Zuniga Javier Jesus, Juarez Caballero Grelda Yazmin. Artificial Intelligence Perspectives on Mexican Art: A Case Study. Am J Sci Eng Technol. 2023;8(1):63-70. doi: 10.11648/j.ajset.20230801.17

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@article{10.11648/j.ajset.20230801.17,
  author = {Espinosa Zuniga Javier Jesus and Juarez Caballero Grelda Yazmin},
  title = {Artificial Intelligence Perspectives on Mexican Art: A Case Study},
  journal = {American Journal of Science, Engineering and Technology},
  volume = {8},
  number = {1},
  pages = {63-70},
  doi = {10.11648/j.ajset.20230801.17},
  url = {https://doi.org/10.11648/j.ajset.20230801.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajset.20230801.17},
  abstract = {In this article a set of images corresponding to paintings of eight painters considered an Artistic Heritage of Mexico was clustered to identify clusters of images with similar characteristics between themselves. The images were acquired from a public source available on the Internet, a Pre-processing phase was applied in order to standardize the images in size and number of pixels, an extraction phase of features was applied for each image using Principal Components Analysis (PCA) and Histograms of Oriented Gradients (HOG), a segmentation phase of the features that were derived in the extraction phase was applied using the K-Means technique and the quality of the clusters that were obtained was evaluated using the Silhouette measure. As a result, seven clusters were attained with interesting characteristics: two of the most renowned Mexican painters worldwide whose artistic work is known for using a rich variety of shapes and colors (Diego Rivera and Frida Kahlo) clearly predominated in two clusters; an artist who is recognized for capturing Mexican landscapes in his paintings (José María Velasco) predominated in another cluster; in other three clusters a mixture of various Mexican artists predominated and in the last cluster Diego Rivera clearly predominated. According to the results, it seems that the paintings of Diego Rivera stand out due to a greater number of shapes used compared to the rest of the paintings analyzed. This article is a sample of the potential of Artificial Intelligence applied to Mexican art (and to art in general).},
 year = {2023}
}

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TY  - JOUR
T1  - Artificial Intelligence Perspectives on Mexican Art: A Case Study
AU  - Espinosa Zuniga Javier Jesus
AU  - Juarez Caballero Grelda Yazmin
Y1  - 2023/03/09
PY  - 2023
N1  - https://doi.org/10.11648/j.ajset.20230801.17
DO  - 10.11648/j.ajset.20230801.17
T2  - American Journal of Science, Engineering and Technology
JF  - American Journal of Science, Engineering and Technology
JO  - American Journal of Science, Engineering and Technology
SP  - 63
EP  - 70
PB  - Science Publishing Group
SN  - 2578-8353
UR  - https://doi.org/10.11648/j.ajset.20230801.17
AB  - In this article a set of images corresponding to paintings of eight painters considered an Artistic Heritage of Mexico was clustered to identify clusters of images with similar characteristics between themselves. The images were acquired from a public source available on the Internet, a Pre-processing phase was applied in order to standardize the images in size and number of pixels, an extraction phase of features was applied for each image using Principal Components Analysis (PCA) and Histograms of Oriented Gradients (HOG), a segmentation phase of the features that were derived in the extraction phase was applied using the K-Means technique and the quality of the clusters that were obtained was evaluated using the Silhouette measure. As a result, seven clusters were attained with interesting characteristics: two of the most renowned Mexican painters worldwide whose artistic work is known for using a rich variety of shapes and colors (Diego Rivera and Frida Kahlo) clearly predominated in two clusters; an artist who is recognized for capturing Mexican landscapes in his paintings (José María Velasco) predominated in another cluster; in other three clusters a mixture of various Mexican artists predominated and in the last cluster Diego Rivera clearly predominated. According to the results, it seems that the paintings of Diego Rivera stand out due to a greater number of shapes used compared to the rest of the paintings analyzed. This article is a sample of the potential of Artificial Intelligence applied to Mexican art (and to art in general).
VL  - 8
IS  - 1
ER  -

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