Automatic photo cropping is an important tool for improving visual quality of
digital photos without resorting to tedious manual selection. Traditionally,
photo cropping is accomplished by determining the best proposal window through
visual quality assessment or saliency detection. In essence, the performance of
an image cropper highly depends on the ability to correctly rank a number of
visually similar proposal windows. Despite the ranking nature of automatic
photo cropping, little attention has been paid to learning-to-rank algorithms
in tackling such a problem. In this work, we conduct an extensive study on
traditional approaches as well as ranking-based croppers trained on various
image features. In addition, a new dataset consisting of high quality cropping
and pairwise ranking annotations is presented to evaluate the performance of
various baselines. The experimental results on the new dataset provide useful
insights into the design of better photo cropping algorithms.

本研究通过对传统方法和基于排名的图像剪裁器的研究，讨论了基于学习排名算法处理自动图像剪裁问题。此外，作者还提出了一个新数据集，以评估各种基线算法的表现。实验结果为设计更好的自动剪裁算法提供了有价值的见解。