# Learning to Compose with Professional Photographs on the Web

## TL;DR

This paper introduces a deep ranking network that learns aesthetic preferences from professional photographs to improve photo composition without explicit rules, achieving state-of-the-art results.

## Contribution

It proposes a view finding approach trained on web-mined professional photos, implicitly capturing photographic rules for aesthetic assessment.

## Key findings

- Achieves state-of-the-art performance on image cropping datasets
- Effectively learns aesthetic preferences without explicit photographic rules
- Demonstrates the effectiveness of web-mined professional photographs for training

## Abstract

Photo composition is an important factor affecting the aesthetics in photography. However, it is a highly challenging task to model the aesthetic properties of good compositions due to the lack of globally applicable rules to the wide variety of photographic styles. Inspired by the thinking process of photo taking, we formulate the photo composition problem as a view finding process which successively examines pairs of views and determines their aesthetic preferences. We further exploit the rich professional photographs on the web to mine unlimited high-quality ranking samples and demonstrate that an aesthetics-aware deep ranking network can be trained without explicitly modeling any photographic rules. The resulting model is simple and effective in terms of its architectural design and data sampling method. It is also generic since it naturally learns any photographic rules implicitly encoded in professional photographs. The experiments show that the proposed view finding network achieves state-of-the-art performance with sliding window search strategy on two image cropping datasets.