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AADB
Classification
Aesthetics
|...
License: Custom

Overview

This dataset is a new aesthetics and attributes database (AADB) which contains aesthetic scores
and meaningful attributes assigned to each image by multiple human raters. Anonymized rater
identities are recorded across images allowing us to exploit intra-rater consistency using
a novel sampling strategy when computing the ranking loss of training image pairs. We show
the proposed sampling strategy is very effective and robust in face of subjective judgement
of image aesthetics by individuals with different aesthetic tastes. Experiments demonstrate
that our unified model can generate aesthetic rankings that are more consistent with human
ratings. To further validate our model, we show that by simply thresholding the estimated aesthetic
scores, we are able to achieve state-or-the-art classification performance on the existing
AVA dataset benchmark.

Data Collection

To collect a large and varied set of photographic images, we download images from the Flickr
website1 which carry a Creative Commons license and manually curate the data set to remove
non-photographic images (e.g. cartoons, drawings, paintings, ads images, adult-content images,
etc.). We have five different workers then independently annotate each image with an overall
aesthetic score and a fixed set of eleven meaningful attributes using Amazon Mechanical Turk
(AMT)2 . The AMT raters work on batches, each of which contains ten images. For each image,
we average the ratings of five raters as the ground-truth aesthetic score. The number of images
rated by a particular worker follows long tail distribution.

Citation

@inproceedings{kong2016aesthetics,
    Author = {Kong, Shu and Shen, Xiaohui and Lin, Zhe and Mech, Radomir and Fowlkes, Charless},
    Title = {Photo Aesthetics Ranking Network with Attributes and Content Adaptation},
    Booktitle = {European Conference on Computer Vision (ECCV)},
    Year = {2016}
}

License

Custom

Data Summary
Type
Image,
Amount
--
Size
1.12GB
Provided by
UC Irvine
In 1965, the University of California, Irvine was founded with a mission to catalyze the community and enhance lives through rigorous academics, cutting-edge research, and dedicated public service. Today, we draw on the unyielding spirit of our pioneering faculty, staff and students who arrived on campus with a dream to inspire change and generate new ideas.
| Amount -- | Size 1.12GB
AADB
Classification
Aesthetics
License: Custom

Overview

This dataset is a new aesthetics and attributes database (AADB) which contains aesthetic scores
and meaningful attributes assigned to each image by multiple human raters. Anonymized rater
identities are recorded across images allowing us to exploit intra-rater consistency using
a novel sampling strategy when computing the ranking loss of training image pairs. We show
the proposed sampling strategy is very effective and robust in face of subjective judgement
of image aesthetics by individuals with different aesthetic tastes. Experiments demonstrate
that our unified model can generate aesthetic rankings that are more consistent with human
ratings. To further validate our model, we show that by simply thresholding the estimated aesthetic
scores, we are able to achieve state-or-the-art classification performance on the existing
AVA dataset benchmark.

Data Collection

To collect a large and varied set of photographic images, we download images from the Flickr
website1 which carry a Creative Commons license and manually curate the data set to remove
non-photographic images (e.g. cartoons, drawings, paintings, ads images, adult-content images,
etc.). We have five different workers then independently annotate each image with an overall
aesthetic score and a fixed set of eleven meaningful attributes using Amazon Mechanical Turk
(AMT)2 . The AMT raters work on batches, each of which contains ten images. For each image,
we average the ratings of five raters as the ground-truth aesthetic score. The number of images
rated by a particular worker follows long tail distribution.

Citation

@inproceedings{kong2016aesthetics,
    Author = {Kong, Shu and Shen, Xiaohui and Lin, Zhe and Mech, Radomir and Fowlkes, Charless},
    Title = {Photo Aesthetics Ranking Network with Attributes and Content Adaptation},
    Booktitle = {European Conference on Computer Vision (ECCV)},
    Year = {2016}
}

License

Custom

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