论文标题
照片评估者：带有深度学习的自动选择器的照片
Photo Rater: Photographs Auto-Selector with Deep Learning
论文作者
论文摘要
Photo Rater是一个计算机视觉项目，它使用神经网络帮助摄影师在基于同一场景拍摄的照片中选择最佳照片。这个过程通常被称为摄影中的“淘汰”，如果手动完成，它可能乏味且耗时。照片评分者利用三个独立的神经网络来完成这项任务：一个用于一般图像质量评估，一个用于对照片进行分类是模糊的（由于手的不稳定还是过量的），另一种用于评估一般美学（包括照片的组成）。通过每个神经网络馈送图像后，照片评估者为每个图像输出最终分数，根据此分数对它们进行排名并将其呈现给用户。
Photo Rater is a computer vision project that uses neural networks to help photographers select the best photo among those that are taken based on the same scene. This process is usually referred to as "culling" in photography, and it can be tedious and time-consuming if done manually. Photo Rater utilizes three separate neural networks to complete such a task: one for general image quality assessment, one for classifying whether the photo is blurry (either due to unsteady hands or out-of-focusness), and one for assessing general aesthetics (including the composition of the photo, among others). After feeding the image through each neural network, Photo Rater outputs a final score for each image, ranking them based on this score and presenting it to the user.
