Photo Rater: Photographs Auto-Selector with Deep Learning
This addresses a tedious task for photographers, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles the problem of automating photo selection (culling) for photographers by developing Photo Rater, a system that uses three neural networks to assess image quality, blurriness, and aesthetics, outputting scores to rank photos and reduce manual effort.
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.