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September 10, 2025

Research on emotion recognition of art design based on fusion features and transfer learning

Key Points

The proposed method enhances emotion recognition performance by 8% and increases accuracy by 5%.
Experimental evaluations on the Flickr dataset validate the effectiveness of the proposed approach.
The framework utilizes a multi-branch convolutional network to integrate low-level and semantic features.
Transfer learning techniques are employed to ensure strong generalization across diverse artistic styles.

Abstract

This study proposes a fusion feature extraction and transfer learning-based framework for emotion recognition in art and design. We extract low-level visual features (color and texture) and semantic features, and integrate them using a multi-branch convolutional network. Transfer learning techniques are employed to enhance recognition performance on small-scale artistic datasets. Experimental evaluations on the Flickr dataset demonstrate that our method improves recognition efficiency by 8% and increases accuracy by 5% compared to traditional approaches. The proposed approach shows strong generalization across diverse artistic styles, offering a promising solution for emotion analysis in creative industries.

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