Abstract
Much research has been concerned with the notion of bottom-up saliency in visual scenes, i.e. the contribution of low-level image features such as brightness, colour, contrast, and motion to the deployment of attention. Because the human visual system is obviously highly optimized for the real world, it is reasonable to draw inspiration from human behaviour in the design of machine vision algorithms that determine regions of relevance. In previous work, we were able to show that a very simple and generic grayscale video representation, namely the geometric invariants of the structure tensor, predicts eye movements when viewing dynamic natural scenes better than complex, state-of-the-art models. Here, we moderately increase the complexity of our model and compute the invariants for colour videos, i.e. on the multispectral structure tensor and for different colour spaces. Results show that colour slightly improves predictive power.
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Dorr, M., Vig, E., Barth, E. (2012). Colour Saliency on Video. In: Suzuki, J., Nakano, T. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32615-8_59
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DOI: https://doi.org/10.1007/978-3-642-32615-8_59
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