Ma Fake, Li Huwei
Henan Economy and Trade Vocational College, Zhengzhou, China.
PeerJ Comput Sci. 2023 Jul 26;9:e1462. doi: 10.7717/peerj-cs.1462. eCollection 2023.
In modern education, mental health problems have become the focus and difficulty of students' education. Painting therapy has been integrated into the school's art education as an effective mental health intervention. Deep learning can automatically learn the image features and abstract the low-level image features into high-level features. However, traditional image classification models are prone to lose background information, resulting in poor adaptability of the classification model. Therefore, this article extracts the lost colour of painting images based on K-means clustering and proposes a painting style classification model based on an improved convolutional neural network (CNN), where a modified Synthetic Minority Oversampling Technique (SMOTE) is proposed to amplify the data. Then, the CNN network structure is optimized by adjusting the network's vertical depth and horizontal width. Finally, a new activation function, PPReLU, is proposed to suppress the excessive value of the positive part. The experimental results show that the proposed model has the highest accuracy in classifying painting image styles by comparing it with state-of-the-art methods, whose accuracy is up to 91.55%, which is 8.7% higher than that of traditional CNN.
在现代教育中，心理健康问题已成为学生教育的重点和难点。绘画疗法作为一种有效的心理健康干预手段已融入学校艺术教育。深度学习能够自动学习图像特征，并将低级图像特征抽象为高级特征。然而，传统图像分类模型容易丢失背景信息，导致分类模型适应性较差。因此，本文基于K均值聚类提取绘画图像中丢失的颜色，并提出一种基于改进卷积神经网络（CNN）的绘画风格分类模型，其中提出了一种改进的合成少数过采样技术（SMOTE）来扩充数据。然后，通过调整网络的垂直深度和水平宽度来优化CNN网络结构。最后，提出一种新的激活函数PPReLU来抑制正值部分的过大值。实验结果表明，与现有方法相比，所提模型在绘画图像风格分类方面具有最高的准确率，其准确率高达91.55%，比传统CNN高8.7%。
