Spatially regularized active diffusion learning for high-dimensional images
Spatially regularized active diffusion learning for high-dimensional images
复制标题
DOI：
10.1016/j.patrec.2020.04.021
复制
发表时间：
2019-11
期刊：
影响因子：
--
通讯作者：
James M. Murphy
中科院分区：
文献类型：
--
作者：
James M. Murphy
提出了一种用于高维图像分类的主动学习方法，该方法使用空间正则化的非线性扩散几何来表征簇核。所提出的方法从估计的聚类核中采样，以便生成一组小但有效的训练标签，这些训练标签通过潜在的扩散过程传播到数据集的其余部分。通过对图像丰富的高维光谱信息进行空间正则化来有效地估计数据中最重要和最有影响力的点，我们的方法避免了训练数据集中的冗余。这使得它可以用非常少的训练标签来产生高精度的标签。该算法是一种有效的数值实现，在合适的数据模型下，数据点的数量基本上是线性的，并且在真实的高光谱图像上具有最先进的性能。
An active learning method for the classification of high-dimensional images is proposed in which spatially-regularized nonlinear diffusion geometry is used to characterize cluster cores. The proposed method samples from estimated cluster cores in order to generate a small but potent set of training labels which propagate to the remainder of the dataset via the underlying diffusion process. By spatially regularizing the rich, high-dimensional spectral information of the image to efficiently estimate the most significant and influential points in the data, our approach avoids redundancy in the training dataset. This allows it to produce high-accuracy labelings with a very small number of training labels. The proposed algorithm admits an efficient numerical implementation that scales essentially linearly in the number of data points under a suitable data model and enjoys state-of-the-art performance on real hyperspectral images.