Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs
This paper develops a graph-based pipeline for label-efficient multispectral and hyperspectral image segmentation. Pixels or local image patches are embedded as nodes of a similarity graph, and graph Laplace learning propagates the small number of queried labels across the image.
Jun 1, 2024
Novel batch active learning approach and its application to synthetic aperture radar datasets
The paper introduces a two-stage strategy for making batch active learning nearly as accurate as sequential querying while substantially reducing the number of classifier updates. Dijkstra’s Annulus Core-Set (DAC) first constructs a representative candidate set; LocalMax then selects a diverse batch by enforcing local maxima of the acquisition function on the data graph.
Jun 13, 2023