Design of Chinese painting style classification model based on multi-layer aggregation CNN

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PeerJ Computer Science

Main article text

 

Introduction

Emotional characteristics in tcp

Color

Shape and texture

Tcp emotion classification model based on cnn

Feature region extraction

Optimized CNN structure

Visual classification

Experiment and analysis

Dataset

Model training

Training parameters

Training results

Emotion classification results

Conclusion

Supplemental Information

Additional Information and Declarations

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Xiaofang Du conceived and designed the experiments, performed the experiments, prepared figures and/or tables, and approved the final draft.

Yangfeng Cai conceived and designed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The code is available in the Supplemental File.

The data is available at Zenodo: Hong, Y. (2022). annotation data for art painting detection and identification (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6551801.

Funding

The authors received no funding for this work.

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