DBSCAN聚类算法在图像风格迁移中的应用

Application of DBSCAN Clustering Algorithm in Image Style Transfer

尽管图像风格迁移技术在不断发展,但现阶段对于图像风格迁移的效果而言仍有较大的提升空间。为了使图像在风格迁移后具有与目标风格图像更贴近的颜色特征,本文基于GAN (Generative Ad-versarial Network)模型提出了一种使用DBSCAN (Density-Based Spatial Clustering of Application with Noise)聚类算法定义颜色数量的损失函数并用于约束模型的训练,从而使生成的图像更趋近于目标风格图像。实验结果表明在引入由DBSCAN算法定义的颜色数量损失函数后图像风格迁移效果得到了更好的提升,生成图像在颜色上更具有原风格图像的特征,视觉上更具立体感。

Although the image style transfer technology is constantly developing, there is still much room for improvement in the effect of image style transfer. In order to make the image have similar color characteristics with the target style image after the style transfer, this paper introduces a color-quantity-criteria defined by the DBSCAN (Density-Based Spatial Clustering of Application with Noise) clustering algorithm. The transfer model is designed based on the GAN (Generative Adversarial Network). The color-quantity-loss-function is used to constrain the training of the model, so that the generated image is closer to the style image in the number of colors. The experimental results show that the image style transfer effect of the proposed algorithm is better improved, and the generated image not only has the characteristics of the original style image in color but also has more stereoscopic visual effect.