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• 1. [Article] Photo Quality Assessment: Predicting Crowd Opinions

  Existing methods for photo quality assessment typically formulate photo quality assessment as a binary classification problem that labels a photo as low- or high-quality. Photo quality assessment, however, ...

  Citation × Citation Citation Abstract Copy Title: Photo Quality Assessment: Predicting Crowd Opinions Author: Mai, Uyen T. Year: 2016 Existing methods for photo quality assessment typically formulate photo quality assessment as a binary classification problem that labels a photo as low- or high-quality. Photo quality assessment, however, is subjective, and people often rate a photo differently. Therefore, the quality of a photo sometimes cannot be fully described by a low- or high-quality label. In this paper, we present a subjective photo quality assessment method that predicts how a group of users rates a photo. Specifically, our method predicts a quality score distribution that is likely produced by a group of people rating the photo. Our method models the score distribution using the mean and standard deviation. Our method uses a regression approach and integrates a wide spectrum of image features, including manually crafted features, generic image features, and deep learning features, to predict the mean score and standard deviation. We experiment our method on the large scale AVA dataset where each photo on average is rated by 200 users with score ranges from 1-10. Our experiment shows that our regression approach can predict the mean score and standard deviation with RMSE errors 0.67 and 0.19, respectively. Title: Photo Quality Assessment: Predicting Crowd Opinions Author: Mai, Uyen T. Year: 2016 Existing methods for photo quality assessment typically formulate photo quality assessment as a binary classification problem that labels a photo as low- or high-quality. Photo quality assessment, however, is subjective, and people often rate a photo differently. Therefore, the quality of a photo sometimes cannot be fully described by a low- or high-quality label. In this paper, we present a subjective photo quality assessment method that predicts how a group of users rates a photo. Specifically, our method predicts a quality score distribution that is likely produced by a group of people rating the photo. Our method models the score distribution using the mean and standard deviation. Our method uses a regression approach and integrates a wide spectrum of image features, including manually crafted features, generic image features, and deep learning features, to predict the mean score and standard deviation. We experiment our method on the large scale AVA dataset where each photo on average is rated by 200 users with score ranges from 1-10. Our experiment shows that our regression approach can predict the mean score and standard deviation with RMSE errors 0.67 and 0.19, respectively. Close

• 2. [Image] The Millrace Studios (11 of 23)

  	Millrace 3 which contains the photo lab.
  	Citation × Citation Citation Abstract Copy Title: The Millrace Studios (11 of 23) Author: unknown Year: 2015 Millrace 3 which contains the photo lab. Title: The Millrace Studios (11 of 23) Author: unknown Year: 2015 Millrace 3 which contains the photo lab. Close

• 3. [Image] Duck mascot, 2014

  	Color photo of the University of Oregon Duck mascot posing for a photo at a men's basketball game on January 12, 2013 at Matthew Knight Arena.
  	Citation × Citation Citation Abstract Copy Title: Duck mascot, 2014 Year: 2014 Color photo of the University of Oregon Duck mascot posing for a photo at a men's basketball game on January 12, 2013 at Matthew Knight Arena. Title: Duck mascot, 2014 Year: 2014 Color photo of the University of Oregon Duck mascot posing for a photo at a men's basketball game on January 12, 2013 at Matthew Knight Arena. Close

• 4. [Image] Duck mascot, 2014

  	Color photo of the University of Oregon Duck mascot posing for a photo with a young fan wearing a Duck head at a men's basketball game on January 30, 2014 at Matthew Knight Arena.
  	Citation × Citation Citation Abstract Copy Title: Duck mascot, 2014 Year: 2014 Color photo of the University of Oregon Duck mascot posing for a photo with a young fan wearing a Duck head at a men's basketball game on January 30, 2014 at Matthew Knight Arena. Title: Duck mascot, 2014 Year: 2014 Color photo of the University of Oregon Duck mascot posing for a photo with a young fan wearing a Duck head at a men's basketball game on January 30, 2014 at Matthew Knight Arena. Close

• 5. [Image] Design Library - 4 of 17

  	Stock image
  	Citation × Citation Citation Abstract Copy Title: Design Library - 4 of 17 Author: unknown Year: 2014 Stock image Title: Design Library - 4 of 17 Author: unknown Year: 2014 Stock image Close

• 6. [Image] Design Library - 10 of 17

  	Stock image
  	Citation × Citation Citation Abstract Copy Title: Design Library - 10 of 17 Author: unknown Year: 2014 Stock image Title: Design Library - 10 of 17 Author: unknown Year: 2014 Stock image Close

• 7. [Image] Service desk in Design Library - 2 of 2

  	Stock image
  	Citation × Citation Citation Abstract Copy Title: Service desk in Design Library - 2 of 2 Author: unknown Year: 2014 Stock image Title: Service desk in Design Library - 2 of 2 Author: unknown Year: 2014 Stock image Close

• 8. [Image] Design Library - 2 of 17

  	Stock image
  	Citation × Citation Citation Abstract Copy Title: Design Library - 2 of 17 Author: unknown Year: 2014 Stock image Title: Design Library - 2 of 17 Author: unknown Year: 2014 Stock image Close

• 9. [Image] Design Library - 17 of 17

  	Stock image
  	Citation × Citation Citation Abstract Copy Title: Design Library - 17 of 17 Author: unknown Year: 2014 Stock image Title: Design Library - 17 of 17 Author: unknown Year: 2014 Stock image Close

• 10. [Image] Bookeye in Design Library - 2 of 2

  	Stock image
  	Citation × Citation Citation Abstract Copy Title: Bookeye in Design Library - 2 of 2 Author: unknown Year: 2014 Stock image Title: Bookeye in Design Library - 2 of 2 Author: unknown Year: 2014 Stock image Close