What type of study is this?

This is a In Vitro Study study.

August 17, 2025

Spatio-structural tensor learning for image artistic style analysis via enhanced multilinear feature fusion

Key Points

Achieved average recognition accuracy of 96.2% for oil painting style recognition, showing significant improvement.
Recognition outcome surpassed comparison models, providing a robust method for identifying oil painting styles.
Methodology involved enhanced fuzzy clustering, fourth-order tensor training, and support vector machine for classification.
Results suggest promising applications in art analysis and automated style classification, with potential for further research.

Abstract

Focusing on the issue of loss of image spatial structure information in the current oil painting style recognition research, this paper first preprocesses the oil painting image, then combines the spatial information and grayscale information to improve the fuzzy clustering algorithm (FCM) (EFCM), and utilizes the EFCM to segment the oil painting image. On this basis, the fourth-order tensor training samples are constructed for the segmented oil painting images, and the oil painting image features are extracted using multilinear principal component analysis (MPCA). Finally, the support vector machine (SVM) optimized by the improved particle swarm algorithm (EAPSO) is used for oil painting style recognition. The simulation outcome demonstrates that the offered model has an average recognition accuracy (mAP) of 96.2%, which is better than the comparison model, and provides a new technical path for the accurate recognition of oil painting style.

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