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Computer Science > Computer Vision and Pattern Recognition

arXiv:2211.14420 (cs)
This paper has been withdrawn by Charlie Ruan
[Submitted on 26 Nov 2022 (v1), last revised 16 Oct 2023 (this version, v2)]

Title:Photo Rater: Photographs Auto-Selector with Deep Learning

Authors:Wentao Guo, Charlie Ruan, Claire Zhou
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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.
Comments: The authors discovered issues in the code that produced figures 8 and 9
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2211.14420 [cs.CV]
  (or arXiv:2211.14420v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2211.14420

Submission history

From: Charlie Ruan [view email]
[v1] Sat, 26 Nov 2022 00:55:52 UTC (13,292 KB)
[v2] Mon, 16 Oct 2023 21:27:20 UTC (1 KB) (withdrawn)
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