Abstract
AbstractGraph learning, when used as a semi-supervised learning (SSL) method, performs well for classification tasks with a low label rate. We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi- or hyperspectral image segmentation. Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification. This work builds on recent advances in the design of novel active learning acquisition functions (e.g., the Model Change approach in arXiv:2110.07739) while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods. In addition to improvements in the accuracy, our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.
Funder
University of California, Los Angeles
National Defense Science and Engineering Graduate
Los Alamos National Laboratory
National Geospatial-Intelligence Agency
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Applied Mathematics
Reference45 articles.
1. Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching in fixed dimensions. J. ACM 45(6), 891–923 (1998). https://doi.org/10.1145/293347.293348
2. Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006)
3. Bertozzi, A.L., Flenner, A.: Diffuse interface models on graphs for classification of high dimensional data. Multiscale Model. Simul. 10(3), 1090–1118 (2012)
4. Bertozzi, A.L., Hosseini, B., Li, H., Miller, K., Stuart, A.M.: Posterior consistency of semi-supervised regression on graphs. Inverse Problems 37(10), 105011 (2021)
5. Bertozzi, A.L., Luo, X., Stuart, A.M., Zygalakis, K.C.: Uncertainty quantification in graph-based classification of high dimensional data. SIAM/ASA J. Uncertain. Quantif. 6(2), 568–595 (2018)
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献