Camera Obscurer: Generative Art for Design Inspiration

  • Dilpreet Singh
  • Nina Rajcic
  • Simon ColtonEmail author
  • Jon McCormack
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11453)


We investigate using generated decorative art as a source of inspiration for design tasks. Using a visual similarity search for image retrieval, the Camera Obscurer app enables rapid searching of tens of thousands of generated abstract images of various types. The seed for a visual similarity search is a given image, and the retrieved generated images share some visual similarity with the seed. Implemented in a hand-held device, the app empowers users to use photos of their surroundings to search through the archive of generated images and other image archives. Being abstract in nature, the retrieved images supplement the seed image rather than replace it, providing different visual stimuli including shapes, colours, textures and juxtapositions, in addition to affording their own interpretations. This approach can therefore be used to provide inspiration for a design task, with the abstract images suggesting new ideas that might give direction to a graphic design project. We describe a crowdsourcing experiment with the app to estimate user confidence in retrieved images, and we describe a pilot study where Camera Obscurer provided inspiration for a design task. These experiments have enabled us to describe future improvements, and to begin to understand sources of visual inspiration for design tasks.



We would like to thank the participants in the pilot study for their time and energy, members of SensiLab for their very useful feedback on the Camera Obscurer app, and the anonymous reviewers for their helpful comments.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.SensiLab, Faculty of ITMonash UniversityMelbourneAustralia
  2. 2.Game AI Group, EECSQueen Mary University of LondonLondonUK

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