AVA: A Large-Scale Database for Aesthetic Visual Analysis

Naila Murray, Luca Marchesotti, Florent Perronnin
IEEE CVPR 2012

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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.

Paper

paper

Dataset

AVA Database (zip, 4.2 MB)

The zip file contains:

Copyright Considerations
Rights to all images are retained by the photographers/dpchallenge. This is why the image files are not included in the dataset. Please respect the copyright and refrain from redistributing images or data. N. Murray, L. Marchesotti, and F. Perronnin. AVA: A large-scale database for aesthetic visual analysis. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 2408–2415. IEEE, 2012

Benchmarks

Benchmark results using two state-of-the-art (SIFT and color-based based) image descriptors are shown in the plots below. Please see the paper for further details.

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Reference

Citation:N. Murray, L. Marchesotti, F. Perronnin. AVA: A Large-Scale Database for Aesthetic Visual Analysis. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 2408-2415. IEEE, 2012.

Bibtex:
@InProceedings{MMP2012,
  author       = "Naila Murray and Luca Marchesotti and Florent Perronnin",
  title        = "AVA: A Large-Scale Database for Aesthetic Visual Analysis",
  booktitle    = "Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on",
  pages        = "2408--2415",
  year         = "2012",
  organization = "IEEE"
}

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