论文标题
以人为中心的图像裁剪，具有分区感和内容的功能
Human-centric Image Cropping with Partition-aware and Content-preserving Features
论文作者
论文摘要
图像裁剪旨在在图像中找到视觉吸引力的作物，这是一项重要但具有挑战性的任务。在本文中，我们考虑了一种特定而实用的应用：以人为中心的图像种植，重点是对一个人的描绘。为此，我们提出了一种以人为中心的图像裁剪方法，该方法具有两种新型候选作物的特征设计：分区感知功能和内容保留功能。对于分区感知功能，我们将整个图像分为基于人体边界框的九个分区，并在人类信息上以不同条件的候选作物中的不同分区处理。对于内容提供内容的功能，我们预测一个热图，表明要包含在良好农作物中的重要内容，并提取热图和候选作物之间的几何关系。广泛的实验表明，我们的方法可以在以人为中心的图像裁剪任务上对最新的图像裁剪方法有利。代码可从https://github.com/bcmi/human-centric-image-cropping获得。
Image cropping aims to find visually appealing crops in an image, which is an important yet challenging task. In this paper, we consider a specific and practical application: human-centric image cropping, which focuses on the depiction of a person. To this end, we propose a human-centric image cropping method with two novel feature designs for the candidate crop: partition-aware feature and content-preserving feature. For partition-aware feature, we divide the whole image into nine partitions based on the human bounding box and treat different partitions in a candidate crop differently conditioned on the human information. For content-preserving feature, we predict a heatmap indicating the important content to be included in a good crop, and extract the geometric relation between the heatmap and a candidate crop. Extensive experiments demonstrate that our method can perform favorably against state-of-the-art image cropping methods on human-centric image cropping task. Code is available at https://github.com/bcmi/Human-Centric-Image-Cropping.
