Abstract
This paper describes novel, interdisciplinary work towards learning the properties of visual scenes that restore our directed attention from fatigue. A groundtruth dataset of images rated for restorative potential was constructed and validated using human subjects, and biologically-inspired image features were used to train a number of regression models for this rating. The trained models were used to predict the restorative potential of unseen images and the predictions were tested using human subjects, with promising results.
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Mountstephens, J. (2014). Using Biologically-Inspired Visual Features To Model The Restorative Potential Of Scenes. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_24
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DOI: https://doi.org/10.1007/978-3-319-12643-2_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12642-5
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