The objective of this work is to detect shadows in images. We pose this as the problem of labeling image regions, where each region corresponds to a group of superpixels. To predict the label of each region, we train a kernel Least-Squares Support Vector Machine (LSSVM) for separating shadow and non-shadow regions. The parameters of the kernel and the classifier are jointly learned to minimize the leave-one-out cross validation error. Optimizing the leave-one-out cross validation error is typically difficult, but it can be done efficiently in our framework. Experiments on two challenging shadow datasets, UCF and UIUC, show that our region classifier outperforms more complex methods. We further enhance the performance of the region classifier by embedding it in a Markov Random Field (MRF) framework and adding pairwise contextual cues. This leads to a method that outperforms the state-of-the-art for shadow detection. In addition we propose a new method for shadow removal based on region relighting. For each shadow region we use a trained classifier to identify a neighboring lit region of the same material. Given a pair of lit-shadow regions we perform a region relighting transformation based on histogram matching of luminance values between the shadow region and the lit region. Once a shadow is detected, we demonstrate that our shadow removal approach produces results that outperform the state of the art by evaluating our method using a publicly available benchmark dataset.

中文翻译：

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用于阴影检测和去除的留一内核优化


这项工作的目的是检测图像中的阴影。我们将此视为标记图像区域的问题，其中每个区域对应于一组超像素。为了预测每个区域的标签，我们训练了一个内核最小二乘支持向量机（LSSVM）来分离阴影和非阴影区域。共同学习内核和分类器的参数，以最小化留一交叉验证误差。优化留一交叉验证错误通常很困难，但可以在我们的框架中有效地完成。在两个具有挑战性的阴影数据集 UCF 和 UIUC 上进行的实验表明，我们的区域分类器优于更复杂的方法。我们通过将区域分类器嵌入到马尔可夫随机场（MRF）框架中并添加成对上下文线索，进一步增强了区域分类器的性能。这导致了一种优于最先进的阴影检测方法。此外，我们提出了一种基于区域重新照明的阴影去除新方法。对于每个阴影区域，我们使用经过训练的分类器来识别相同材质的相邻照亮区域。给定一对光照-阴影区域，我们基于阴影区域和光照区域之间的亮度值的直方图匹配来执行区域重新光照变换。一旦检测到阴影，我们就通过使用公开可用的基准数据集评估我们的方法来证明我们的阴影去除方法产生的结果优于现有技术。