Bio-Inspired Models of Network, Information, and Computing Systems. 5th International ICST Conference, BIONETICS 2010, Boston, USA, December 1-3, 2010, Revised Selected Papers

Research Article

Colour Saliency on Video

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  • @INPROCEEDINGS{10.1007/978-3-642-32615-8_59,
        author={Michael Dorr and Eleonora Vig and Erhardt Barth},
        title={Colour Saliency on Video},
        proceedings={Bio-Inspired Models of Network, Information, and Computing Systems. 5th International ICST Conference, BIONETICS 2010, Boston, USA, December 1-3, 2010, Revised Selected Papers},
        proceedings_a={BIONETICS},
        year={2012},
        month={10},
        keywords={video saliency eye movements intrinsic dimension multispectral structure tensor},
        doi={10.1007/978-3-642-32615-8_59}
    }
    
  • Michael Dorr
    Eleonora Vig
    Erhardt Barth
    Year: 2012
    Colour Saliency on Video
    BIONETICS
    Springer
    DOI: 10.1007/978-3-642-32615-8_59
Michael Dorr,*, Eleonora Vig1,*, Erhardt Barth1,*
  • 1: University of Lübeck
*Contact email: michael.dorr@schepens.harvard.edu, vig@inb.uni-luebeck.de, barth@inb.uni-luebeck.de

Abstract

Much research has been concerned with the notion of bottom-up aliency 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.