Synthesized computational aesthetic evaluation of photos

Wu, Weining; Cai, D. S.; Wang, Li; Huang, Qinghua; Xu, Xiangmin; Li, Xuelong

doi:10.1016/j.neucom.2014.12.106

Cited by 30 publications

(25 citation statements)

References 36 publications

(55 reference statements)

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“…Some authors, such as Datta et al [3], Wang et al [4], Ke et al [5], and Luo et al [6], conducted studies aimed at automated aesthetic classification using a number of technical characteristics such as lightness, saturation, Rule of Thirds, etc. For these experiments, sets of large-format photographs from websites and the evaluations made by the users of such sites were used.…”

Section: State Of the Artmentioning

confidence: 99%

Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach

et al. 2019

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An important topic in evolutionary art is the development of systems that can mimic the aesthetics decisions made by human begins, e.g., fitness evaluations made by humans using interactive evolution in generative art. This paper focuses on the analysis of several datasets used for aesthetic prediction based on ratings from photography websites and psychological experiments. Since these datasets present problems, we proposed a new dataset that is a subset of DPChallenge.com. Subsequently, three different evaluation methods were considered, one derived from the ratings available at DPChallenge.com and two obtained under experimental conditions related to the aesthetics and quality of images. We observed different criteria in the DPChallenge.com ratings, which had more to do with the photographic quality than with the aesthetic value. Finally, we explored learning systems other than state-of-the-art ones, in order to predict these three values. The obtained results were similar to those using state-of-the-art procedures.

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Section: State Of the Artmentioning

confidence: 99%

Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach

et al. 2019

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“…There are some datasets that have been used in several times for the images classification. Among them, Photo.net [3][4][5], DPChallenge.com [6,7] and the one created by Cela-Conde et al [8][9][10] However, when its generalization capacity is studied, it has been detected that they cannot be considered as representative for the realization of image experiments. In some cases, the correlation is greater when the validation set belongs to the same data source as the training set, and this correlation drops markedly when the validation source set is different from that of the training.…”

Section: Limitations Found In the Datasets Availablementioning

confidence: 99%

Dataset for the Aesthetic Value Automatic Prediction

Rodríguez-Fernández

Santos

Torrente

2019

The 2nd XoveTIC Conference (XoveTIC 2019)

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One of the most relevant issue in the prediction and classification of the aesthetic value of an image is the sample set used to train and validate the computational system. In this document the limitations found in different datasets used to classificate and predict aesthetic values are exposed, and a new dataset is proposed with images from the DPChallenge.com portal, with evaluations of three different populations.

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“…Except for the six models mentioned above, some types of machine-learning models, such as deep learning, artificial neural network, and support vector machine, have been used for photograph aesthetical evaluation and prediction [ 57 – 62 ] Although the machine-learning models are black boxes, they are more convenient for model building because of their availability and generality with the development during the last decades. Nearly, all software programs for machine learning, such as MATLAB, Weka, and Enterprise Miner, provide neural network toolbox.…”

Section: Models Of Aesthetic Appreciationmentioning

confidence: 99%

Toward Model Building for Visual Aesthetic Perception

Liu

Lughofer

Zeng

2017

Computational Intelligence and Neuroscience

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Several models of visual aesthetic perception have been proposed in recent years. Such models have drawn on investigations into the neural underpinnings of visual aesthetics, utilizing neurophysiological techniques and brain imaging techniques including functional magnetic resonance imaging, magnetoencephalography, and electroencephalography. The neural mechanisms underlying the aesthetic perception of the visual arts have been explained from the perspectives of neuropsychology, brain and cognitive science, informatics, and statistics. Although corresponding models have been constructed, the majority of these models contain elements that are difficult to be simulated or quantified using simple mathematical functions. In this review, we discuss the hypotheses, conceptions, and structures of six typical models for human aesthetic appreciation in the visual domain: the neuropsychological, information processing, mirror, quartet, and two hierarchical feed-forward layered models. Additionally, the neural foundation of aesthetic perception, appreciation, or judgement for each model is summarized. The development of a unified framework for the neurobiological mechanisms underlying the aesthetic perception of visual art and the validation of this framework via mathematical simulation is an interesting challenge in neuroaesthetics research. This review aims to provide information regarding the most promising proposals for bridging the gap between visual information processing and brain activity involved in aesthetic appreciation.

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Synthesized computational aesthetic evaluation of photos

Cited by 30 publications

References 36 publications

Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach

Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach

Dataset for the Aesthetic Value Automatic Prediction

Toward Model Building for Visual Aesthetic Perception

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