# Composition-Aware Image Aesthetics Assessment

arXiv: https://arxiv.org/abs/1907.10801 · HTML: https://mlsanity.com/paper/1907.10801/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2019.

## Built on by (most cited first, 15 of 15)

- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models](https://mlsanity.com/paper/2305.10843/) (2023)
- [Image Composition Assessment with Saliency-augmented Multi-pattern Pooling](https://mlsanity.com/paper/2104.03133/) (2021)
- [A Hypothesis for the Aesthetic Appreciation in Neural Networks](https://mlsanity.com/paper/2108.02646/) (2021)
- [Composition and Style Attributes Guided Image Aesthetic Assessment](https://mlsanity.com/paper/2111.04647/) (2021)
- [FRIH: Fine-grained Region-aware Image Harmonization](https://mlsanity.com/paper/2205.06448/) (2022)
- [Image Aesthetics Assessment Using Graph Attention Network](https://mlsanity.com/paper/2206.12869/) (2022)
- [Detecting Visual Design Principles in Art and Architecture through Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.04048/) (2021)
- [Graph Neural Networks in Computer Vision -- Architectures, Datasets and Common Approaches](https://mlsanity.com/paper/2212.10207/) (2022)
- [Series Photo Selection via Multi-view Graph Learning](https://mlsanity.com/paper/2203.09736/) (2022)
- [Enhancing Historical Image Retrieval with Compositional Cues](https://mlsanity.com/paper/2403.14287/) (2024)
- [A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction](https://mlsanity.com/paper/2010.07661/) (2020)
- [Pseudo-labelling and Meta Reweighting Learning for Image Aesthetic Quality Assessment](https://mlsanity.com/paper/2201.02714/) (2022)
- [Exploring CNN-based models for image's aesthetic score prediction with using ensemble](https://mlsanity.com/paper/2210.05119/) (2022)
- [From Concepts to Judgments: Interpretable Image Aesthetic Assessment](https://mlsanity.com/paper/2603.18108/) (2026)

## Abstract (arXiv)

Automatic image aesthetics assessment is important for a wide variety of applications such as on-line photo suggestion, photo album management and image retrieval. Previous methods have focused on mapping the holistic image content to a high or low aesthetics rating. However, the composition information of an image characterizes the harmony of its visual elements according to the principles of art, and provides richer information for learning aesthetics. In this work, we propose to model the image composition information as the mutual dependency of its local regions, and design a novel architecture to leverage such information to boost the performance of aesthetics assessment. To achieve this, we densely partition an image into local regions and compute aesthetics-preserving features over the regions to characterize the aesthetics properties of image content. With the feature representation of local regions, we build a region composition graph in which each node denotes one region and any two nodes are connected by an edge weighted by the similarity of the region features. We perform reasoning on this graph via graph convolution, in which the activation of each node is determined by its highly correlated neighbors. Our method naturally uncovers the mutual dependency of local regions in the network training procedure, and achieves the state-of-the-art performance on the benchmark visual aesthetics datasets.