Aesthetic assessment is subjective, and the distribution of the aesthetic levels is imbalanced. In order to realize the auto-assessment of photo aesthetics, we focus on retraining the CNN-based aesthetic assessment model by dropping out the unavailable samples in the middle levels from the training data set repetitively to overcome the effect of imbalanced aesthetic data on classification. Further, the method of extracting aesthetics highlight region of the photo image by using the two repetitively trained models is presented. Therefore, the correlation of the extracted region with the aesthetic levels is analyzed to illustrate what aesthetics features influence the aesthetic quality of the photo. Moreover, the testing data set is from the different data source called 500px. Experimental results show that the proposed method is effective.
翻译：审美评估是主观的,而且审美水平的分布不平衡。为了实现光学美学的自动评估,我们侧重于对有线电视新闻网的审美评估模型进行再培训,从培训数据集中反复删除中层无法取得的样本,以克服不平衡的审美数据对分类的影响。此外,还介绍了利用两个重复培训模型提取审美数据的方法,突出照片图像的区域。因此,对提取区域与审美水平的相互关系进行了分析,以说明美学对照片的审美质量有何影响。此外,测试数据集来自称为500px的不同数据源。实验结果显示,拟议方法是有效的。
相关内容
微信扫码咨询专知VIP会员