Photo Rater: Photographs Auto-Selector with Deep Learning

TL;DR

Photo Rater employs deep neural networks to automate photo culling by evaluating quality, blurriness, and aesthetics, thereby streamlining the selection process for photographers.

Contribution

It introduces a multi-network deep learning system specifically designed for automated photo ranking and selection in photography workflows.

Findings

Achieves effective photo ranking based on quality, blur, and aesthetics.

Reduces manual effort in photo culling process.

Demonstrates high correlation with human judgment in photo quality assessment.

Abstract

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.