High level describable attributes for predicting aesthetics and interestingness

By Sagnik Dhar, Vicente Ordonez, Tamara L. Berg. 2011

525 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Social Psychology

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Citations in the corpus

Recognizing Image Style (2013, methodology): “Following Dhar et al. [6], who use high-level classiﬁers as features for their aesthetic rating prediction task, we evaluate using object classiﬁer conﬁdences as features.”
Streetscore -- Predicting the Perceived Safety of One Million Streetscapes (2014, background): “Research in visual analysis beyond semantics, such as interestingness [5], beauty [4] and memorability [10], has been a recent topic of interest in computer vision.”
Interestingness Prediction by Robust Learning to Rank (2014, background): “Early efforts on image interestingness prediction focus on different aspects than interestingness as such, including image quality [22], memorability [19], and aesthetics [7].”
RAPID: Rating Pictorial Aesthetics using Deep Learning (2014, background): “Various handcrafted aesthetics-relevant features have been proposed [6, 13, 21, 3, 20, 7, 26, 27], including low-level image statistics such as distributions of edges and color histograms, and high-level photographic rules such as the rule of thirds.”
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning (2020, background): “designed three hand-crafted rules, including attributes of composition, content, and skyillumination to approximate both aesthetics and interestingness of images [10].”
Exploring Principles-of-Art Features For Image Emotion Recognition (2014, background): “Aesthetics and interestingness are predicted through high level describable attributes, including compositional, content and sky-illumination attributes [8].”
Salient object detection: A survey (2014, background): “nclude: object importance [26]–[28], memorability [29], scene clutter [30], video interestingness [31]–[34], surprise [35], image quality assessment [36], [37], scene typicality [38], [39], aesthetic [33], and attributes [40]. An enormous amount of research has been undertaken to study attention thr”
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”
Photo Aesthetics Ranking Network with Attributes and Content Adaptation (2016, background): “Early works formulate aesthetic analysis as a classification or a regression problem of mapping images to aesthetic ratings provided by human raters [5,11,18,6,26].”
What makes an image popular? (2014, background): “Similarly, image quality and aesthetics are other attributes that have been recently explored in substantial detail [6, 8, 3].”
New Contour Cue-Based Hybrid Sparse Learning for Salient Object Detection (2019, methodology): “We implement GS and PSO on our own and for CRF we use the MATLAB implementation in [63].”
Adopting Abstract Images for Semantic Scene Understanding (2016, background): “In this work we study semantic importance, saliency and memorability of objects, and the relationships of these high-level concepts with each other.”
Image Aesthetic Assessment: An experimental survey (2016, methodology): “(2011) [17] 16000-image private set from DPChallenge PR-curve Nishiyama et al.”
Deep Learning the City: Quantifying Urban Perception at a Global Scale (2016, background): “There is a growing body of literature on predicting the perceptual responses to images , such as aesthetics [21], memorability [22], interestingness [23], and virality [24].”
Recognizing and Curating Photo Albums via Event-Specific Image Importance (2017, background): “Image importance is a complex image property which is related to various factors, such as aesthetics [15], interestingness [5] and image memorability [11].”
Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder (2015, background): “Nevertheless, for certain tasks, such shared attributes [14], [12], [25], [10], [46] may simply be unavailable or nontrivial to deﬁne.”
What Makes a Photograph Memorable? (2014, unknown): “Index Terms—Scene understanding, image memorability, global image features, attributes ✦”