Exploration of Deep Semantic Analysis and Application of Video Images in Visual Communication Design Based on Multimodal Feature Fusion Algorithm

Fully utilizing image and video semantic processing techniques can play a more effective role in visual communication design. In order to further explore the application of multimodal feature fusion algorithm (MFF) in video image feature analysis in visual communication design, with the aim of enhancing the depth and breadth of design creation. This article focuses on the application of video semantic understanding technology by combining image and video semantic processing techniques, in order to achieve a comprehensive, three-dimensional, and open expansion of design thinking. The MFF algorithm was proposed and implemented, which innovatively integrates multimodal information such as visual and audio in videos, deeply explores action semantics, and shows significant performance improvements compared to traditional algorithms. Specifically, compared to the other two mainstream algorithms, its performance has improved by 24.33% and 14.58%, respectively. This discovery not only validates the superiority of MFF algorithm in the field of video semantic understanding, but also reveals the profound impact of video semantic understanding technology on visual communication design practice, providing new perspectives and tools for the design industry and promoting innovation and development of design thinking. The novelty of this study lies in its interdisciplinary methodology, which applies advanced algorithm techniques to the field of art and design, and the significant improvement of the proposed MFF algorithm in enhancing design efficiency and creativity.

@article{Chen2024,
  title     = {Exploration of Deep Semantic Analysis and Application of Video Images in Visual Communication Design Based on Multimodal Feature Fusion Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yanlin Chen and Xiwen Chen},
  doi       = {10.14569/IJACSA.2024.01508103},
  url       = {https://doi.org/10.14569/IJACSA.2024.01508103}
}