Image Aesthetic Assessment: An experimental survey
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TL;DR: In this article, a review of computer vision techniques used in the assessment of image aesthetic quality is presented, which aims at computationally distinguishing high quality from low quality photos based on photographic rules, typically in the form of binary classification or quality scoring.
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Abstract: This article reviews recent computer vision techniques used in the assessment of image aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high-quality from low-quality photos based on photographic rules, typically in the form of binary classification or quality scoring. A variety of approaches has been proposed in the literature to try to solve this challenging problem. In this article, we summarize these approaches based on visual feature types (hand-crafted features and deep features) and evaluation criteria (data set characteristics and evaluation metrics). The main contributions and novelties of the reviewed approaches are highlighted and discussed. In addition, following the emergence of deep-learning techniques, we systematically evaluate recent deep-learning settings that are useful for developing a robust deep model for aesthetic scoring.
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Citations
Seamless Capturing of Moments Using Photographic Compositions and Image Aesthetics
Deok-Ho Kim,Taehyuk Kwon,Byeong-Wook Yoo,Gun-Ill Lee,Won-Woo Lee,Jae-Woong Lee,Sunghun Yim,Jiwon Jeong +7 more
- 01 Jan 2020
TL;DR: The proposed method classifies nine photographic compositions for captured images, and enhances aesthetic quality of the images classified to one of nine compositions to determine that the image is able to be a good photo.
Survey: Deep Learning for Video Aesthetics
TL;DR: In this paper, the authors review the deep learning techniques which effectively automate the video and image aesthetics analysis and achieve an impressive performance in automated aesthetics analysis in comparison to Handcrafted features.
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Exploring to establish an appropriate model for image aesthetic assessment via CNN-based RSRL: An empirical study.
TL;DR: In this paper, an algorithm of determining the optimal model from the multiple photo score prediction models generated by CNN-based repetitively self-revised learning (RSRL) is proposed.
•Book
A Survey of Blur Detection and Sharpness Assessment Methods
Juan Andrade
- 05 Jan 2021
TL;DR: The main causes of blurring in images include: (a) the existence of objects at different depths within the scene which are known to exist, and (b) the fact that the scene is known as mentioned in this paper.
Exploring CNN-based models for image's aesthetic score prediction with using ensemble
TL;DR: The experimental results verify that the proposed framework of constructing two types of the automatic image aesthetics assessment (IAA) models with different CNN architectures and improving the performance of the image’s aesthetic score (AS) prediction by the ensemble is effective.
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