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Illumination Estimation Is Sufficient for Indoor-Outdoor Image Classification

  • Nikola BanićEmail author
  • Sven Lončarić
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11269)

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.

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Image Processing Group, Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia

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