Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America.
PLoS One. 2013 Aug 20;8(8):e71715. doi: 10.1371/journal.pone.0071715. eCollection 2013.
We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.
我们旨在通过在区域凝聚过程中使用机器学习工具来改进分割。我们提出了一种从超像素执行层次凝聚分割的主动学习方法。我们的方法在凝聚过程的所有尺度上结合了多个特征，适用于任意维数的数据，并且可以扩展到非常大的数据集。我们主张使用信息变异来衡量分割准确性，特别是在神经组织的 3D 电子显微镜（EM）图像中，并使用该指标在 EM 和自然图像中证明优于竞争算法。
