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

<p>Civic and political elements play an indispensable role in the process of national cultural development. In the context of the era of booming Internet technology, this paper proposes the extraction technology of the Civic and Political elements of digital art design and explores the effective way of disseminating the Civic and Political elements in the Internet. The mean drift clustering algorithm is introduced to the image to segment the source image into multiple independent and disconnected image sub-blocks to achieve the segmentation pre-processing of the image. Subsequently, the density peak clustering algorithm is used to design the main color extraction algorithm, separate the image from the background by Otsu threshold segmentation and hole filling algorithm, and perform color clustering on the image to achieve the feature extraction of Civic and Political elements. This algorithm’s extraction time efficiency is superior to that of Grabcut, LazySnapping, and RepSnapping algorithms, and the correct extraction rate is over 85%. Taking the digital artworks published on the Internet as of December 2023 as the main research body, the implication of ideological and political elements is analyzed. The number of occurrences and extractions of each Civic and Political element is in the interval of 400~900, and the concentration of Civic and Political embodiment has been improved, but there is still much room for improvement.</p>
</abstract>

Civic and political elements play an indispensable role in the process of national cultural development. In the context of the era of booming Internet technology, this paper proposes the extraction technology of the Civic and Political elements of digital art design and explores the effective way of disseminating the Civic and Political elements in the Internet. The mean drift clustering algorithm is introduced to the image to segment the source image into multiple independent and disconnected image sub-blocks to achieve the segmentation pre-processing of the image. Subsequently, the density peak clustering algorithm is used to design the main color extraction algorithm, separate the image from the background by Otsu threshold segmentation and hole filling algorithm, and perform color clustering on the image to achieve the feature extraction of Civic and Political elements. This algorithm’s extraction time efficiency is superior to that of Grabcut, LazySnapping, and RepSnapping algorithms, and the correct extraction rate is over 85%. Taking the digital artworks published on the Internet as of December 2023 as the main research body, the implication of ideological and political elements is analyzed. The number of occurrences and extractions of each Civic and Political element is in the interval of 400~900, and the concentration of Civic and Political embodiment has been improved, but there is still much room for improvement.

The Embodiment of the Elements of Thinking and Politics in Digital Art Design in the Internet Era

Applied Mathematics and Nonlinear Sciences

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

Civic and political elements play an indispensable role in the process of national cultural development. In the context of the era of booming Internet...

The Embodiment of the Elements of Thinking and Politics in Digital Art Design in the Internet Era

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 16. Apr. 2024

Akzeptiert: 01. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-1965

Schlüsselwörter
Mean drift clustering, Hole filling algorithm, Threshold segmentation, Color clustering, Ideological and political elements

© 2024 Xiaojing Song, published by Sciendo

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

The Embodiment of the Elements of Thinking and Politics in Digital Art Design in the Internet Era

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 16. Apr. 2024

Akzeptiert: 01. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-1965

SchlüsselwörterMean drift clustering, Hole filling algorithm, Threshold segmentation, Color clustering, Ideological and political elements

© 2024 Xiaojing Song, published by Sciendo

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

Schlüsselwörter
Mean drift clustering, Hole filling algorithm, Threshold segmentation, Color clustering, Ideological and political elements