111 lines
6.9 KiB
BibTeX
111 lines
6.9 KiB
BibTeX
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@article{dcnn,
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title = {Photo {Aesthetics} {Analysis} via {DCNN} {Feature} {Encoding}},
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volume = {19},
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issn = {1941-0077},
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doi = {10.1109/TMM.2017.2687759},
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abstract = {We propose an automatic framework for quality assessment of a photograph as well as analysis of its aesthetic attributes. In contrast to the previous methods that rely on manually designed features to account for photo aesthetics, our method automatically extracts such features using a pretrained deep convolutional neural network (DCNN). To make the DCNN-extracted features more suited to our target tasks of photo quality assessment and aesthetic attribute analysis, we propose a novel feature encoding scheme, which supports vector machines-driven sparse restricted Boltzmann machines, which enhances sparseness of features and discrimination between target classes. Experimental results show that our method outperforms the current state-of-the-art methods in automatic photo quality assessment, and gives aesthetic attribute ratings that can be used for photo editing. We demonstrate that our feature encoding scheme can also be applied to general object classification task to achieve performance gains.},
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number = {8},
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journal = {IEEE Transactions on Multimedia},
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author = {Lee, Hui-Jin and Hong, Ki-Sang and Kang, Henry and Lee, Seungyong},
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month = aug,
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year = {2017},
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note = {Conference Name: IEEE Transactions on Multimedia},
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keywords = {Aesthetic attributes, deep convolutional neural network (DCNN), Encoding, feature encoding, Feature extraction, Mathematical model, Neural networks, photo aesthetics, Quality assessment, restricted Boltzmann machines, Support vector machines, Training},
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pages = {1921--1932},
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annote = {This paper discusses the use of a Convolution Neural Network to predict how aesthetic a given picture is.},
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file = {IEEE Xplore Abstract Record:/home/noble/Zotero/storage/E6YUFLQE/7886320.html:text/html},
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}
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@inproceedings{ava_paper,
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title = {{AVA}: {A} large-scale database for aesthetic visual analysis},
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shorttitle = {{AVA}},
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doi = {10.1109/CVPR.2012.6247954},
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abstract = {With the ever-expanding volume of visual content available, the ability to organize and navigate such content by aesthetic preference is becoming increasingly important. While still in its nascent stage, research into computational models of aesthetic preference already shows great potential. However, to advance research, realistic, diverse and challenging databases are needed. To this end, we introduce a new large-scale database for conducting Aesthetic Visual Analysis: AVA. It contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style. We show the advantages of AVA with respect to existing databases in terms of scale, diversity, and heterogeneity of annotations. We then describe several key insights into aesthetic preference afforded by AVA. Finally, we demonstrate, through three applications, how the large scale of AVA can be leveraged to improve performance on existing preference tasks.},
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booktitle = {2012 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
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author = {Murray, Naila and Marchesotti, Luca and Perronnin, Florent},
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month = jun,
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year = {2012},
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note = {ISSN: 1063-6919},
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keywords = {Communities, Image color analysis, Semantics, Social network services, Visual databases, Visualization},
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pages = {2408--2415},
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annote = {A paper that discusses the creation of the AVA dataset. It's composed of 250 thousand aesthetic images collected from a photography competition website along with grades for aesthetic features.},
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file = {IEEE Xplore Abstract Record:/home/noble/Zotero/storage/HE3EFVJV/6247954.html:text/html},
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}
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@misc{AADB,
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title = {Photo {Aesthetics} {Ranking} {Network} with {Attributes} and {Content} {Adaptation}},
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url = {https://github.com/aimerykong/deepImageAestheticsAnalysis},
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abstract = {ECCV2016 - fine-grained photo aesthetics rating with interpretability},
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urldate = {2022-02-11},
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author = {Kong, Shu},
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month = jan,
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year = {2022},
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note = {original-date: 2016-06-05T06:08:10Z},
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annote = {A tool that uses the AADB dataset to predict the aesthetics of a given picture using a CNN.},
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}
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@misc{pytorch,
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title = {{PyTorch}},
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url = {https://www.pytorch.org},
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abstract = {An open source machine learning framework that accelerates the path from research prototyping to production deployment.},
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language = {en},
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urldate = {2022-02-11},
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author = {{Adam Paszke} and {Sam Gross} and {Soumith Chintala} and {Gregory Chanan}},
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annote = {An open-source machine learning framework developed by Facebook (based on the Torch library)},
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file = {Snapshot:/home/noble/Zotero/storage/HPSFJGU3/pytorch.org.html:text/html},
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}
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@misc{opencv,
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title = {{OpenCV}},
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url = {https://opencv.org},
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abstract = {OpenCV provides a real-time optimized Computer Vision library, tools, and hardware. It also supports model execution for Machine Learning (ML) and Artificial Intelligence (AI).},
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language = {en-US},
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urldate = {2022-02-11},
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journal = {OpenCV},
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author = {{Intel Corporation} and {Willow Garage} and {Itseez}},
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annote = {An open source library used for real-time computer vision.},
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file = {Snapshot:/home/noble/Zotero/storage/JYX6KIPC/opencv.org.html:text/html},
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}
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@misc{ava_downloader,
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title = {{AVA} {Dataset}},
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url = {https://github.com/imfing/ava_downloader},
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abstract = {:arrow\_double\_down: Download AVA dataset (A Large-Scale Database for Aesthetic Visual Analysis)},
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urldate = {2022-02-11},
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author = {Fing},
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month = feb,
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year = {2022},
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note = {original-date: 2016-11-13T02:20:32Z},
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keywords = {aesthetic-visual-analysis, ava, computer-vision, dataset, python},
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annote = {A project with tools required to build the AVA dateset.},
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}
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@misc{image_quality_assessment,
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title = {Image {Quality} {Assessment}},
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copyright = {Apache-2.0},
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url = {https://github.com/idealo/image-quality-assessment},
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abstract = {Convolutional Neural Networks to predict the aesthetic and technical quality of images.},
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urldate = {2022-02-11},
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publisher = {idealo},
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month = feb,
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year = {2022},
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note = {original-date: 2018-06-12T14:46:09Z},
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keywords = {aws, computer-vision, convolutional-neural-networks, deep-learning, e-commerce, idealo, image-quality-assessment, keras, machine-learning, mobilenet, neural-network, nima, tensorflow},
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annote = {An open source tool developed by Idealo which uses a CNN to predict the aesthetic value of a given image.},
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}
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@misc{tensorflow,
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title = {{TensorFlow}},
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url = {https://www.tensorflow.org/},
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urldate = {2022-02-11},
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author = {{Google Brain Team}},
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annote = {Open-source library for machine learning and AI developed by Google.},
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}
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@misc{flutter,
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title = {Flutter - {Build} apps for any screen},
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url = {https://flutter.dev/},
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abstract = {Flutter transforms the entire app development process. Build, test, and deploy beautiful mobile, web, desktop, and embedded apps from a single codebase.},
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urldate = {2022-02-11},
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}
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