Sample-specific repetitive learning for photo aesthetic assessment and highlight region extraction

TL;DR

This paper introduces a novel repetitive learning approach for photo aesthetic assessment that addresses data imbalance and extracts highlight regions to analyze aesthetic features, demonstrating improved effectiveness.

Contribution

It proposes a repetitive retraining method to handle imbalanced aesthetic data and introduces a technique for extracting aesthetic highlight regions using two models.

Findings

The method effectively improves aesthetic assessment accuracy.

Highlight regions correlate with aesthetic levels.

Experimental results validate the approach's effectiveness.

Abstract

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.