Assessing the aesthetic quality of photographs using generic image descriptors

By Luca Marchesotti, Florent Perronnin, Diane Larlus, Gabriella Csurka. Vision, 2011

403 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/13c10779f9c85bc1665edc17ead57dcd03f592e4

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In Cognitive Neuroscience

In Computer Vision and Pattern Recognition

Citations in the corpus

DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks (2014, background): “There is also work trying to model “non-conventional” concepts or properties of the images, such as image aesthetic and quality [16, 22], memorability [12], interestingness [12], and aﬀection/emotions [21, 35, 13, 21, 35, 37].”
Memorability of Image Regions (2012, background): “Recently, learning algorithms have been proposed to automatically interpret whether an image is aesthetically pleasant or not [20, 3], memorable or forgettable [7, 6], and the role that other high level photographic properties plays in image interpretation (photo quality [19], attractiveness [16], c”
Blind Image Quality Assessment Based on High Order Statistics Aggregation (2016, positive): “High order statistics have been applied to many computer vision applications, including, for example, image classification [34], image retrieval [35], [36] and image aesthetic evaluation [37].”
Learning beautiful (and ugly) attributes (2013, methodology): “We therefore propose to use our learned attributes to make such a prediction and compare to the approach of [18] which is based on generic image features and it is to date the most performing baseline on AVA dataset.”
A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping (2019, methodology): “Instead of using hand-crafted features, another option [49], [50] for image”
Image Aesthetic Assessment: An experimental survey (2016, methodology): “Conventional approaches [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22] typically adopt handcrafted features to computationally model the photographic rules (lighting, contrast), global image layout (rule-of-thirds) and typical objects (human profiles, animals,”
Photo Aesthetics Ranking Network with Attributes and Content Adaptation (2016, methodology): “SIFT [14] and Fisher Vector [28,27]), that have been found to outperform methods using rule-based features [21].”
Large-scale visual sentiment ontology and detectors using adjective noun pairs (2013, background): “The closest that comes to sentiment analysis for visual content is the analysis of aesthetics [6, 15, 22], interestingness [12], and affect or emotions [13, 21, 37, 36].”
RAPID: Rating Pictorial Aesthetics using Deep Learning (2014, methodology): “Earlier visual aesthetics assessment research focused on examining handcrafted visual features based on common cues such as color [6, 26, 27], texture [6, 13], composition [21, 20, 7], and content [20, 7], as well as generic image descriptors [23, 31, 24].”