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Actionable Information in Vision

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Machine Learning for Computer Vision

Part of the book series: Studies in Computational Intelligence ((SCI,volume 411))

Abstract

A notion of visual information is introduced as the complexity not of the raw images, but of the images after the effects of nuisance factors such as viewpoint and illumination are discounted. It is rooted in ideas of J. J. Gibson, and stands in contrast to traditional information as entropy or coding length of the data regardless of its use, and regardless of the nuisance factors affecting it. The non-invertibility of nuisances such as occlusion and quantization induces an “information gap” that can only be bridged by controlling the data acquisition process. Measuring visual information entails early vision operations, tailored to the structure of the nuisances so as to be “lossless” with respect to visual decision and control tasks (as opposed to data transmission and storage tasks implicit in communications theory). The definition of visual information suggests desirable properties that a visual representation should possess to best accomplish vision-based decision and control tasks.

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  1. University of California, Los Angeles, USA

    Stefano Soatto

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  1. Stefano Soatto
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Correspondence to Stefano Soatto .

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  1. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom

    Roberto Cipolla

  2. Dipartimento di Matematica e Informatica, Università di Catania, Viale Andrea Doria 6, Catania, 95125, Catania, Italy

    Sebastiano Battiato

  3. Dipartimento di Matematica e Informatica, Università di Catania, Viale A. Doria 6, Catania, 95125, Italy

    Giovanni Maria Farinella

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Soatto, S. (2013). Actionable Information in Vision. In: Cipolla, R., Battiato, S., Farinella, G. (eds) Machine Learning for Computer Vision. Studies in Computational Intelligence, vol 411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28661-2_2

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  • DOI: https://doi.org/10.1007/978-3-642-28661-2_2

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