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

ABSTRACT: 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 recogn

SUBMITTER: Wang E 

PROVIDER: S-EPMC11753788 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature