A Machine Learning-Based Approach to Cross-Application of Computer Vision and Visual Communication Design for Automatic Labelling and Classification
Abstract
This study explores the cross-application of computer vision and visual communication design in automatic labeling and classification. First, the basic theories and application scenarios of these two fields are summarized. Secondly, through data preprocessing and feature engineering, a multi-application scenario model is constructed. The experimental design includes baseline comparisons, A/B testing, and user studies to fully evaluate model performance. The results show that the model has significant advantages in improving user experience and information retrieval, but it also has some limitations. This study not only enhances the understanding of the cross-application of these two fields, but also provides a valuable reference for practical application. The model constructed in this study achieved an accuracy of 91.7% and an F1 score of 0.90, which was a significant improvement of 16.3% and 25.0% compared to the baseline model. User satisfaction increased from 4.0 to 4.2 (out of 5). These quantitative indicators confirmed the effectiveness and practicality of the proposed method.References
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https://doi.org/10.31449/inf.v49i6.6963Downloads
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