A Novel Feature Fusion Method for Computing Image Aesthetic Quality

Li, Xueming; Li, Xueming; Zhang, Gang; Zhang, Xianlin

doi:10.1109/access.2020.2983725

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…In Ref. [ 23 ], besides extracting deep CNN features, Li, Li, Zhang and Zhang (2020) propose five algorithms for extracting handcrafted aesthetic feature maps. The aesthetic features and CNN features are fused to improve the aesthetic assessment by designing a novel feature fusion layer.…”

Section: Related Workmentioning

confidence: 99%

Building CNN-Based Models for Image Aesthetic Score Prediction Using an Ensemble

Dai

2023

J. Imaging

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In this paper, we propose a framework that constructs two types of image aesthetic assessment (IAA) models with different CNN architectures and improves the performance of image aesthetic score (AS) prediction by the ensemble. Moreover, the attention regions of the models to the images are extracted to analyze the consistency with the subjects in the images. The experimental results verify that the proposed method is effective for improving the AS prediction. The average F1 of the ensemble improves 5.4% over the model of type A, and 33.1% over the model of type B. Moreover, it is found that the AS classification models trained on the XiheAA dataset seem to learn the latent photography principles, although it cannot be said that they learn the aesthetic sense.

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Section: Related Workmentioning

confidence: 99%

Building CNN-Based Models for Image Aesthetic Score Prediction Using an Ensemble

Dai

2023

J. Imaging

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“…As training from the scratch on relatively small-scale datasets is susceptible to overfitting, most studies tend to use pretrained models for extracting deep features [28][29][30][31]. These pretrained classification networks has already learned on more than one million images.…”

Section: Applying Pre-trained Deep Learning Architecture On Fused Eeg...mentioning

confidence: 99%

Multimodal spatio-temporal-spectral fusion for deep learning applications in physiological time series processing: A case study in monitoring the depth of anesthesia

et al. 2021

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“…Other studies combined neural networks with different algorithms including auto-encoder technique, expert feature knowledge, feature fusion, visual attention, etc. to build new neural network architectures [88][89][90][91][92]. These neural networks leveraged distinct network architectures to automatically extract different image attributes and gave a prediction of aesthetic rating.…”

Section: Image Quality Assessment Based On Deep Neural Networkmentioning

confidence: 99%

Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science

Zhang

2020

Brain Inf.

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The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration.

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A Novel Feature Fusion Method for Computing Image Aesthetic Quality

Cited by 13 publications

References 37 publications

Building CNN-Based Models for Image Aesthetic Score Prediction Using an Ensemble

Building CNN-Based Models for Image Aesthetic Score Prediction Using an Ensemble

Multimodal spatio-temporal-spectral fusion for deep learning applications in physiological time series processing: A case study in monitoring the depth of anesthesia

Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science

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