Sample-specific repetitive learning for photo aesthetic assessment and highlight region extraction
This work addresses the problem of subjective and imbalanced aesthetic assessment in photos, but it appears incremental as it builds on existing CNN-based methods with specific data handling techniques.
The paper tackles photo aesthetic assessment by retraining CNN models with repetitive dropout of unavailable samples from imbalanced aesthetic levels, and extracts highlight regions to analyze aesthetic features. Experimental results on a 500px dataset show the method is effective.
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.