基于图像相似性的双层稀疏编码颜色恒常性
Color Constancy based on Image Similarity via Bilayer Sparse Coding
摘要：
计算颜色恒常性是计算机视觉中一个非常重要的课题，引起了许多研究者的关注。近年来，大量的研究表明，高层次的视觉内容信息的照明估计的影响。然而，所有这些现有的方法本质上是组合策略，其中图像的内容分析仅用于指导各种单独的照度估计方法的组合或选择。在本文中，我们提出了一种新的双层稀疏编码模型的照明估计，同时考虑图像的相似性在低层次的颜色分布和高层次的图像场景内容。为此，将图像的场景内容信息与其颜色分布相结合，以获得最佳的光照估计模型。在两组真实图像上的实验结果表明，该算法优于其他主流的光照估计方法，甚至优于组合方法。
英文摘要：
Computational color constancy is a very important topic in computer vision and has attracted many researchers' attention. Recently, lots of research has shown the effects of high level visual content information for illumination estimation. However, all of these existing methods are essentially combinational strategies in which image's content analysis is only used to guide the combination or selection from a variety of individual illumination estimation methods. In this paper, we propose a novel bilayer sparse coding model for illumination estimation that considers image similarity in terms of both low level color distribution and high level image scene content simultaneously. For the purpose, the image's scene content information is integrated with its color distribution to obtain optimal illumination estimation model. The experimental results on two real-world image sets show that our algorithm is superior to other prevailing illumination estimation methods, even better than combinational methods.
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