@InProceedings{7760382,
  author = {C. Florea and R. Condorovici and C. Vertan and R. Butnaru and L. Florea and R. Vrânceanu},
  booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
  title = {Pandora: Description of a painting database for art movement recognition with baselines and perspectives},
  year = {2016},
  pages = {918-922},
  abstract = {To facilitate computer analysis of visual art, in the form of paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art movement) database, a collection of digitized paintings labelled with respect to the artistic movement. Noting that the set of databases available as benchmarks for evaluation is highly reduced and most existing ones are limited in variability and number of images, we propose a novel large scale dataset of digital paintings. The database consists of more than 7700 images from 12 art movements. Each genre is illustrated by a number of images varying from 250 to nearly 1000. We investigate how local and global features and classification systems are able to recognize the art movement. Our experimental results suggest that accurate recognition is achievable by a combination of various categories.},
  keywords = {feature extraction;image classification;Pandora;painting database description;art movement recognition;visual art computer analysis;image recognition;digital painting large scale dataset;classification system;global feature;local feature;Art;Databases;Painting;Image color analysis;Histograms;Support vector machines;Europe},
  doi = {10.1109/EUSIPCO.2016.7760382},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570255907.pdf},
}

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Noting that the set of databases available as benchmarks for evaluation is highly reduced and most existing ones are limited in variability and number of images, we propose a novel large scale dataset of digital paintings. The database consists of more than 7700 images from 12 art movements. Each genre is illustrated by a number of images varying from 250 to nearly 1000. We investigate how local and global features and classification systems are able to recognize the art movement. 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Vrânceanu},\n booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},\n title = {Pandora: Description of a painting database for art movement recognition with baselines and perspectives},\n year = {2016},\n pages = {918-922},\n abstract = {To facilitate computer analysis of visual art, in the form of paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art movement) database, a collection of digitized paintings labelled with respect to the artistic movement. Noting that the set of databases available as benchmarks for evaluation is highly reduced and most existing ones are limited in variability and number of images, we propose a novel large scale dataset of digital paintings. The database consists of more than 7700 images from 12 art movements. Each genre is illustrated by a number of images varying from 250 to nearly 1000. We investigate how local and global features and classification systems are able to recognize the art movement. 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