Multi-Cue Illumination Estimation via a Tree-Structured Group Joint Sparse Representation
Multi-Cue Illumination Estimation via a Tree-Structured Group Joint Sparse Representation
复制标题
通过树结构组联合稀疏表示的多线索照明估计
DOI：
10.1007/s11263-015-0844-7
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发表时间：
2016
影响因子：
19.5
通讯作者：
Junliang Xing
中科院分区：
文献类型：
--
作者：
Bing Li;Weihua Xiong;Weiming Hu （胡卫明）;Brian Funt;Junliang Xing
提出了一种基于树结构群联合稀疏表示的多线索光照估计方法。测试表明，该方法比现有的方法效果更好，大多数方法仅基于单一线索类型，例如二值化颜色直方图或简单的图像统计量，如平均RGB。大多数现有的照明估计方法仅使用三种线索中的一种进行估计。他们使用的线索类型不同，但所选择的线索要么基于(1)低水平RGB颜色分布的属性，(2)由从属方法提供的中级初始光源估计，或(3)对场景内容的高级知识（例如，室内与室外场景）。提出的多线索方法将这三种线索提供的信息结合在树结构群联合稀疏表示（TGJSR）的框架内。在TGJSR中，训练数据被分组到子组的树状结构中。对未知光源下的测试图像进行特征重构，利用训练数据分组后的联合稀疏表示模型进行特征重构。然后根据联合稀疏表示模型中涉及的权重估计测试图像的照明。作为一个通用框架，建议的TGJSR框架也可以很容易地扩展，以纳入任何新的特征或线索，可能会在未来发现照明估计。
A multi-cue illumination estimation method based on tree-structured group joint sparse representation is proposed. Tests show that the proposed method works better than existing methods, most of which are based on using only a single cue type, for example, a binarized color histogram or simple image statistic such as the mean RGB. Most existing illumination estimation methods make their estimates using only one of three kinds of cues. They differ in which cue type they use, but the chosen cue is either based on (1) properties of the low-level RGB color distribution, (2) mid-level initial illuminant estimates provided by subordinate methods, or (3) high-level knowledge of scene content (e.g., indoor versus outdoor scene). The proposed multi-cue method combines the information provided by cues of all three of these types within the framework of a tree-structured group joint sparse representation (TGJSR). In TGJSR, the training data is grouped into a tree of subgroups. A test image under an unknown illuminant has its features reconstructed in terms of a joint sparse representation model derived from the grouped training data. The test image’s illumination is then estimated based on the weights involved in the joint sparse representation model. As a general framework, the proposed TGJSR framework can also easily be extended to incorporate any new features or cues that might be discovered in the future for illumination estimation.