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The research aims to identify and analyze emotional patterns in artworks through visual image analysis techniques.

Emotion Recognition in Art Creation Using Visual Image Analysis Techniques

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The research aims to identify and analyze emotional patterns in artworks through visual image analysis techniques.
Utilized a dataset of 500 art images representing six emotions.
Images rated by three art experts with high agreement.
Dataset split into 70% training and 30% testing for analysis.
Images resized to 256x256, converted to grayscale, and normalized for feature extraction.
Compared the performance of PCA, LDA, stepwise regression, and a deep learning model.
PCA achieved the highest performance metrics: 94.5% exactness, 95.9% recall, 95.9% accuracy, and 97.6% precision.
LDA and PCA produced low error rates (under 0.1) and followed closely in accuracy measures.
Stepwise regression and deep learning model achieved scores of 0.803 and 0.823 respectively.

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Examines emotional recognition in artworks using visual image analysis, suggesting effective teaching tools for educators.

Zhen Xu (2026) studied this question.

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