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Learning Artistic Lighting Template from Portrait Photographs

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 6314)

Abstract

This paper presents a method for learning artistic portrait lighting template from a dataset of artistic and daily portrait photographs. The learned template can be used for (1) classification of artistic and daily portrait photographs, and (2) numerical aesthetic quality assessment of these photographs in lighting usage. For learning the template, we adopt Haar-like local lighting contrast features, which are then extracted from pre-defined areas on frontal faces, and selected to form a log-linear model using a stepwise feature pursuit algorithm. Our learned template corresponds well to some typical studio styles of portrait photography. With the template, the classification and assessment tasks are achieved under probability ratio test formulations. On our dataset composed of 350 artistic and 500 daily photographs, we achieve a 89.5% classification accuracy in cross-validated tests, and the assessment model assigns reasonable numerical scores based on portraits’ aesthetic quality in lighting.

Keywords

  • Quantile Regression
  • Equal Error Rate
  • Local Contrast
  • Aesthetic Quality
  • Lighting Usage

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Jin, X., Zhao, M., Chen, X., Zhao, Q., Zhu, SC. (2010). Learning Artistic Lighting Template from Portrait Photographs. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15561-1_8

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  • DOI: https://doi.org/10.1007/978-3-642-15561-1_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15560-4

  • Online ISBN: 978-3-642-15561-1

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