Bianco Simone, Ciocca Gianluigi, Cusano Claudio, Schettini Raimondo
Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, 20126 Milano, Italy.
IEEE Trans Image Process. 2008 Dec;17(12):2381-92. doi: 10.1109/TIP.2008.2006661.
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.
在这项工作中，我们研究了如何改进光源估计技术，同时考虑到自动提取的有关图像内容的信息。我们考虑了室内/室外分类，因为这些类别的图像呈现出不同的内容，并且通常在不同的光照条件下拍摄。我们为每个类别设计了不同的策略来选择和调整最合适的算法（或算法组合）。我们还考虑采用一个不确定性类别，该类别对应于室内/室外分类器信心不足的图像。这里考虑的光源估计算法源自Van de Weijer和Gevers最近提出的框架。我们提出了一种自动调整算法参数的程序。我们在广泛使用的Funt和Ciurea数据集的一个合适子集中测试了所提出的策略。实验结果清楚地表明，基于分类的策略优于通用算法。
