Search

Search Results

• 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. [Article] Detecting Rule of Balance in Photography

  Rule of Balance is one of the most important composition rules in photography, which can be used as a standard for photo quality assessment. The rule of balance states that images with evenly distributed ...

  Citation × Citation Citation Abstract Copy Title: Detecting Rule of Balance in Photography Author: Mai, Uyen T., Liu, Feng Year: 2014 Rule of Balance is one of the most important composition rules in photography, which can be used as a standard for photo quality assessment. The rule of balance states that images with evenly distributed visual elements are visually pleasing and thus are highly aesthetic. This work presents a method to automatically classify balanced and unbalanced images. Detecting the rule of balance requires a robust technique to locate and analyze important objects and visual elements, which involves understanding of the image content. Since semantic understanding is currently beyond the state of the art in computer vision, we employ the saliency maps as an alternative. We design a range of features according to the definition and effects of the rule of balance. Our experiments with a variety of machine learning techniques ([8-11]) and saliency analysis methods ([2-6]) demonstrate an encouraging performance in detecting vertical and horizontal balanced images. For future works, the balance detecting system can be developed into a subroutine for an automatic evaluation of professional photography. Title: Detecting Rule of Balance in Photography Author: Mai, Uyen T., Liu, Feng Year: 2014 Rule of Balance is one of the most important composition rules in photography, which can be used as a standard for photo quality assessment. The rule of balance states that images with evenly distributed visual elements are visually pleasing and thus are highly aesthetic. This work presents a method to automatically classify balanced and unbalanced images. Detecting the rule of balance requires a robust technique to locate and analyze important objects and visual elements, which involves understanding of the image content. Since semantic understanding is currently beyond the state of the art in computer vision, we employ the saliency maps as an alternative. We design a range of features according to the definition and effects of the rule of balance. Our experiments with a variety of machine learning techniques ([8-11]) and saliency analysis methods ([2-6]) demonstrate an encouraging performance in detecting vertical and horizontal balanced images. For future works, the balance detecting system can be developed into a subroutine for an automatic evaluation of professional photography. Close