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.

提出了一种用于高维图像分类的主动学习方法，该方法利用空间正则化的非线性扩散几何来表征聚类核心。所提出的方法从估计的聚类核心采样，以生成小的但有效的训练标签集，这些训练标签通过基础的扩散过程传播到数据集的其余部分。通过在空间上规范化图像的丰富，高维光谱信息以有效估计数据中最重要和最重要的点，我们的方法避免了训练数据集中的冗余。这使得它可以使用很少的训练标签来制作高精度标签。