<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The emergence of a vast array of visual artworks meets the growing public demand for cultural literacy while simultaneously presenting challenges in the understanding and selection of appropriate artistic styles. Leveraging advancements in image processing technology, this study successfully automates the classification of 158,652 visual art images by categorizing them into distinct artistic styles. A novel approach combining the Scale-Invariant Feature Transform (SIFT) feature extraction algorithm with the spatial pyramid matching technique is introduced. This paper details the development and evaluation of a classification system tailored for the automatic categorization of visual artworks. Comparative testing results demonstrate that this innovative combination surpasses the traditional SIFT algorithm in both execution speed and classification accuracy. Specifically, the Support Vector Machine (SVM) based classification system, utilizing the enhanced SIFT method, achieves an accuracy rate exceeding 90% in identifying five distinct styles, including PHPS, NOS, IS, TF, and AAS. The implementation of this automatic classification system proves to be a practical tool in aiding individuals to select the visual artwork style that best suits their preferences.</p>
</abstract>

The emergence of a vast array of visual artworks meets the growing public demand for cultural literacy while simultaneously presenting challenges in the understanding and selection of appropriate artistic styles. Leveraging advancements in image processing technology, this study successfully automates the classification of 158,652 visual art images by categorizing them into distinct artistic styles. A novel approach combining the Scale-Invariant Feature Transform (SIFT) feature extraction algorithm with the spatial pyramid matching technique is introduced. This paper details the development and evaluation of a classification system tailored for the automatic categorization of visual artworks. Comparative testing results demonstrate that this innovative combination surpasses the traditional SIFT algorithm in both execution speed and classification accuracy. Specifically, the Support Vector Machine (SVM) based classification system, utilizing the enhanced SIFT method, achieves an accuracy rate exceeding 90% in identifying five distinct styles, including PHPS, NOS, IS, TF, and AAS. The implementation of this automatic classification system proves to be a practical tool in aiding individuals to select the visual artwork style that best suits their preferences.

Design of automatic style classification system of visual art works based on image processing

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The emergence of a vast array of visual artworks meets the growing public demand for cultural literacy while simultaneously presenting challenges in the...

Design of automatic style classification system of visual art works based on image processing

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 28 feb 2024

Accettato: 21 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1590

Parole chiave
Image processing, Automatic classification, Spatial pyramid matching, SIFT algorithm, SVM classification method

© 2024 Jin Dan., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Design of automatic style classification system of visual art works based on image processing

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 28 feb 2024

Accettato: 21 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1590

Parole chiaveImage processing, Automatic classification, Spatial pyramid matching, SIFT algorithm, SVM classification method

© 2024 Jin Dan., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Image processing, Automatic classification, Spatial pyramid matching, SIFT algorithm, SVM classification method