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Parametric art creation platform design based on visual delivery and multimedia data fusion.

Qing Yun1

  • 1School of International Exchange, Kyungil University, Gyeongsan-s, Gyeongsangbuk-do, Republic of South Korea.

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Summary

This study introduces a bimodal time series classification model (BTSCM) for categorizing online artworks. The BTSCM network achieves over 90% accuracy in video classification, improving art content analysis.

Keywords:
Deep Q-network (DQN)Feature fusionFully convolutional network (FCN)Long short-term memory (LSTM)Visual communication

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Multimedia Analysis

Background:

  • Artistic communication increasingly occurs on virtual platforms, necessitating efficient content categorization.
  • Manual labeling of extensive digital art collections is time-consuming and resource-intensive.
  • Existing methods struggle with the complexity of multimodal art content.

Purpose of the Study:

  • To introduce an innovative bimodal time series classification model (BTSCM) for categorizing and labeling artworks on virtual platforms.
  • To leverage multimedia fusion technology for high-precision video classification.
  • To provide a foundation for automated content analysis in digital art platforms.

Main Methods:

  • The BTSCM network classifies video data into image and sound elements.
  • Feature extraction uses Inflated 3D ConvNet for visual data and Mel frequency cepstrum coefficient (MFCC) for audio.
  • Feature synthesis employs a fusion of fully convolutional network (FCN), deep Q-network (DQN), and long short-term memory (LSTM).

Main Results:

  • The BTSCM framework demonstrated outstanding classification results across diverse video datasets.
  • Achieved a classification recognition rate exceeding 90% on a self-established art platform.
  • Outperformed multiple existing multimodal fusion recognition networks.

Conclusions:

  • The BTSCM network offers a highly effective solution for high-precision video classification of artworks.
  • The model provides a significant advancement in the automated scrutiny and annotation of digital art content.
  • This framework lays the groundwork for future research in art creation platform content analysis.