论文标题
基于CNN的重复申请照片的照片“美学不平衡分类”
CNN-based Repetitive self-revised learning for photos' aesthetics imbalanced classification
论文作者
论文摘要
美学评估是主观的，美学水平的分布是不平衡的。为了实现对照片美学的自动评估，我们专注于使用重复性自我修复学习（RSRL）通过不平衡数据集训练基于CNN的美学分类网络。作为RSRL，通过基于先前训练的网络从训练数据集中的中间级别的中间级别删除在美学的中间级别的低似然照片样本，对网络进行了重复训练。此外，保留的两个网络用于提取与美学评估相关的照片的突出显示区域。实验结果表明，基于CNN的重复自我修复学习有效地改善了分类不平衡的性能。
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 using repetitive self-revised learning (RSRL) to train the CNN-based aesthetics classification network by imbalanced data set. As RSRL, the network is trained repetitively by dropping out the low likelihood photo samples at the middle levels of aesthetics from the training data set based on the previously trained network. Further, the retained two networks are used in extracting highlight regions of the photos related with the aesthetic assessment. Experimental results show that the CNN-based repetitive self-revised learning is effective for improving the performances of the imbalanced classification.
