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Related Concept Videos

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Related Experiment Video

Updated: Jun 28, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Improving color constancy using indoor-outdoor image classification.

Simone Bianco1, Gianluigi Ciocca, Claudio Cusano

  • 1Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, 20126 Milano, Italy. bianco@disco.unimib.it

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 14, 2008
PubMed
Summary
This summary is machine-generated.

This study enhances illuminant estimation by using image content, specifically indoor/outdoor classification. Classification-based strategies significantly outperform general algorithms for accurate color and lighting analysis.

Related Experiment Videos

Last Updated: Jun 28, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Accurate illuminant estimation is crucial for color constancy and image analysis.
  • General illuminant estimation algorithms often struggle with diverse image content and varying lighting conditions.
  • Leveraging image content, such as indoor/outdoor classification, can provide context to improve estimation.

Purpose of the Study:

  • To improve illuminant estimation techniques by incorporating automatically extracted image content information.
  • To develop and evaluate classification-based strategies for selecting and tuning illuminant estimation algorithms.
  • To investigate the utility of an uncertainty class for ambiguous images.

Main Methods:

  • Image content analysis using indoor/outdoor classification.
  • Development of strategies for algorithm selection and parameter tuning based on image class.
  • Utilizing an illuminant estimation framework derived from Van de Weijer and Gevers.
  • Automatic tuning of algorithm parameters.
  • Testing on a subset of the Funt and Ciurea dataset.

Main Results:

  • Classification-based strategies for illuminant estimation demonstrate superior performance compared to general-purpose algorithms.
  • Tailoring algorithms to specific image classes (indoor/outdoor) leads to more accurate results.
  • The proposed automatic parameter tuning procedure is effective.
  • The uncertainty class helps manage images where classification is not confident.

Conclusions:

  • Incorporating image content, particularly through indoor/outdoor classification, significantly enhances illuminant estimation accuracy.
  • Class-specific algorithm selection and tuning are more effective than general approaches.
  • The developed strategies offer a robust method for improving color constancy in diverse image sets.