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Analyzing Emotional Semantics of Abstract Art Using Low-Level Image Features

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7014)

Abstract

In this work, we study people’s emotions evoked by viewing abstract art images based on traditional low-level image features within a binary classification framework. Abstract art is used here instead of artistic or photographic images because those contain contextual information that influences the emotional assessment in a highly individual manner. Whether an image of a cat or a mountain elicits a negative or positive response is subjective. After discussing challenges concerning image emotional semantics research, we empirically demonstrate that the emotions triggered by viewing abstract art images can be predicted with reasonable accuracy by machine using a variety of low-level image descriptors such as color, shape, and texture. The abstract art dataset that we created for this work has been made downloadable to the public.

Keywords

  • emotional semantics
  • abstract art
  • psychophysical evaluation
  • image features
  • classification

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Zhang, H., Augilius, E., Honkela, T., Laaksonen, J., Gamper, H., Alene, H. (2011). Analyzing Emotional Semantics of Abstract Art Using Low-Level Image Features. In: Gama, J., Bradley, E., Hollmén, J. (eds) Advances in Intelligent Data Analysis X. IDA 2011. Lecture Notes in Computer Science, vol 7014. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24800-9_38

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  • DOI: https://doi.org/10.1007/978-3-642-24800-9_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24799-6

  • Online ISBN: 978-3-642-24800-9

  • eBook Packages: Computer ScienceComputer Science (R0)