Improving Color Constancy Using Indoor–Outdoor Image Classification

In this work, we investigate how illuminant estimation techniques can be improved, taking into account automatically extracted information about the content of the images. We considered indoor/outdoor classification because the images of these classes present different content and are usually taken under different illumination conditions. We have designed different strategies for the selection and the tuning of the most appropriate algorithm (or combination of algorithms) for each class. We also considered the adoption of an uncertainty class which corresponds to the images where the indoor/outdoor classifier is not confident enough. The illuminant estimation algorithms considered here are derived from the framework recently proposed by Van de Weijer and Gevers. We present a procedure to automatically tune the algorithms' parameters. We have tested the proposed strategies on a suitable subset of the widely used Funt and Ciurea dataset. Experimental results clearly demonstrate that classification based strategies outperform general purpose algorithms.

[1]  Brian V. Funt,et al.  Is Machine Colour Constancy Good Enough? , 1998, ECCV.

[2]  Martin Szummer,et al.  Indoor-outdoor image classification , 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.

[3]  Anil K. Jain,et al.  Image classification for content-based indexing , 2001, IEEE Trans. Image Process..

[4]  Raimondo Schettini,et al.  Consensus-based framework for illuminant chromaticity estimation , 2008, J. Electronic Imaging.

[5]  Robert Michael Lewis,et al.  Pattern Search Methods for Linearly Constrained Minimization , 1999, SIAM J. Optim..

[6]  Michael Schröder,et al.  Automatic Color Correction Based on Generic Content-Based Image Analysis , 2001, CIC.

[7]  Steven D. Hordley,et al.  Scene illuminant estimation: Past, present, and future , 2006 .

[8]  G. Finlayson,et al.  Re-evaluating colour constancy algorithms , 2004, ICPR 2004.

[9]  Raimondo Schettini,et al.  Automatic Classification Of Digital Photographs Based On Decision Forests , 2004, Int. J. Pattern Recognit. Artif. Intell..

[10]  O. SIAMJ.,et al.  ON THE CONVERGENCE OF PATTERN SEARCH ALGORITHMS , 1997 .

[11]  Brian V. Funt,et al.  A Large Image Database for Color Constancy Research , 2003, CIC.

[12]  Raimondo Schettini,et al.  On the detection of pornographic digital images , 2003, Visual Communications and Image Processing.

[13]  Brian V. Funt,et al.  A Comparison of Computational Colour Constancy Algorithms. Part Two: Experiments on Image Data , 2002 .

[14]  Raimondo Schettini,et al.  Color balancing of digital photos using simple image statistics , 2004, Pattern Recognit..

[15]  Graham D. Finlayson,et al.  Shades of Gray and Colour Constancy , 2004, CIC.

[16]  Robert Michael Lewis,et al.  Pattern Search Algorithms for Bound Constrained Minimization , 1999, SIAM J. Optim..

[17]  Robert E. Schapire,et al.  A Brief Introduction to Boosting , 1999, IJCAI.

[18]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[19]  Graham D. Finlayson,et al.  Color by Correlation: A Simple, Unifying Framework for Color Constancy , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Virginia Torczon,et al.  On the Convergence of Pattern Search Algorithms , 1997, SIAM J. Optim..

[21]  Cordelia Schmid,et al.  Using High-Level Visual Information for Color Constancy , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[22]  Graham D. Finlayson,et al.  Color in Perspective , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Brian V. Funt,et al.  A comparison of computational color constancy Algorithms. II. Experiments with image data , 2002, IEEE Trans. Image Process..

[24]  Joost van de Weijer,et al.  Author Manuscript, Published in "ieee Transactions on Image Processing Edge-based Color Constancy , 2022 .

[25]  G. Buchsbaum.  A spatial processor model for object colour perception , 1980 .

[26]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[27]  Brian V. Funt,et al.  White Point Estimation for Uncalibrated Images , 1999, Color Imaging Conference.

[28]  Raimondo Schettini,et al.  Supervised and unsupervised classification post-processing for visual video summaries , 2006, IEEE Transactions on Consumer Electronics.

[29]  Brian V. Funt,et al.  A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data , 2002, IEEE Trans. Image Process..

[30]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[31]  Theo Gevers,et al.  Color Constancy using Natural Image Statistics , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Raimondo Schettini,et al.  A hierarchical classification strategy for digital documents , 2002, Pattern Recognit..