19
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

      Preprint

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Computer vision systems are designed to work well within the context of everyday photography. However, artists often render the world around them in ways that do not resemble photographs. Artwork produced by people is not constrained to mimic the physical world, making it more challenging for machines to recognize. This work is a step toward teaching machines how to categorize images in ways that are valuable to humans. First, we collect a large-scale dataset of contemporary artwork from Behance, a website containing millions of portfolios from professional and commercial artists. We annotate Behance imagery with rich attribute labels for content, emotions, and artistic media. Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation. We believe our Behance Artistic Media dataset will be a good starting point for researchers wishing to study artistic imagery and relevant problems.

          Related collections

          Most cited references5

          • Record: found
          • Abstract: not found
          • Book Chapter: not found

          Microsoft COCO: Common Objects in Context

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Visual Domain Adaptation: A survey of recent advances

              Bookmark
              • Record: found
              • Abstract: not found
              • Conference Proceedings: not found

              High level describable attributes for predicting aesthetics and interestingness

                Bookmark

                Author and article information

                Journal
                2017-04-27
                Article
                1704.08614
                d19595d4-bd3d-4a6a-b443-8c8abafed767

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                cs.CV

                Computer vision & Pattern recognition
                Computer vision & Pattern recognition

                Comments

                Comment on this article