  Applying machine learning tools to digitized image archives has a potential
to revolutionize quantitative research of visual studies in humanities and
social sciences. The ability to process a hundredfold greater number of photos
than has been traditionally possible and to analyze them with an extensive set
of variables will contribute to deeper insight into the material. Overall,
these changes will help to shift the workflow from simple manual tasks to more
demanding stages.
  In this paper, we introduce Automatic Image Content Extraction (AICE)
framework for machine learning-based search and analysis of large image
archives. We developed the framework in a multidisciplinary research project as
framework for future photographic studies by reformulating and expanding the
traditional visual content analysis methodologies to be compatible with the
current and emerging state-of-the-art machine learning tools and to cover the
novel machine learning opportunities for automatic content analysis. The
proposed framework can be applied in several domains in humanities and social
sciences, and it can be adjusted and scaled into various research settings. We
also provide information on the current state of different machine learning
techniques and show that there are already various publicly available methods
that are suitable to a wide-scale of visual content analysis tasks.
