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Illumination Estimation Is Sufficient for Indoor-Outdoor Image Classification

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Pattern Recognition (GCPR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11269))

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Abstract

Indoor-outdoor image classification is a well-known problem for which multiple solutions have been proposed, many of which use both low-level and high-level features put into various models. Despite varying complexity, the accuracy of most of these models is reported to be around 90%. In this paper it is shown that the same accuracy can be obtained by simple manipulation of only low-level features extracted from the image in the early phase of image formation and based on the simplest forms of illumination estimation, namely methods such as Gray-World. Additionally, it is shown how using the built-in camera auto white balance is also enough to effectively achieve state-of-the-art indoor-outdoor classification accuracy. The results are presented and discussed.

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Acknowledgment

We thank the anonymous reviewers for their kind suggestions. This work has been supported by the Croatian Science Foundation under Project IP-06-2016-2092.

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Authors and Affiliations

  1. Image Processing Group, Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, 10000, Zagreb, Croatia

    Nikola Banić & Sven Lončarić

Authors
  1. Nikola Banić
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  2. Sven Lončarić
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Correspondence to Nikola Banić .

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Editors and Affiliations

  1. University of Freiburg, Freiburg im Breisgau, Baden-Württemberg, Germany

    Thomas Brox

  2. University of Stuttgart, Stuttgart, Baden-Württemberg, Germany

    Andrés Bruhn

  3. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

    Mario Fritz

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Banić, N., Lončarić, S. (2019). Illumination Estimation Is Sufficient for Indoor-Outdoor Image Classification. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_33

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  • DOI: https://doi.org/10.1007/978-3-030-12939-2_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12938-5

  • Online ISBN: 978-3-030-12939-2

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