Design of a 3D emotion mapping model for visual feature analysis using improved Gaussian mixture models

Given the integration of color emotion space information from multiple feature sources in multimodal recognition systems, effectively fusing this information presents a significant challenge. This article proposes a three-dimensional (3D) color-emotion space visual feature extraction model for multimodal data integration based on an improved Gaussian mixture model to address these issues. Unlike traditional methods, which often struggle with redundant information and high model complexity, our approach optimizes feature fusion by employing entropy and visual feature sequences. By integrating machine vision with six activation functions and utilizing multiple aesthetic features, the proposed method exhibits strong performance in a high emotion mapping accuracy (EMA) of 92.4%, emotion recognition precision (ERP) of 88.35%, and an emotion recognition F1 score (ERFS) of 96.22%. These improvements over traditional approaches highlight the model’s effectiveness in reducing complexity while enhancing emotional recognition accuracy, positioning it as a more efficient solution for visual emotion analysis in multimedia applications. The findings indicate that the model significantly enhances emotional recognition accuracy.