TY - JOUR
T1 - Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images
AU - Nunez-Iglesias, Juan
AU - Kennedy, Ryan
AU - Parag, Toufiq
AU - Shi, Jianbo
AU - Chklovskii, Dmitri B.
PY - 2013/8/20
Y1 - 2013/8/20
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84882631243&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0071715
DO - 10.1371/journal.pone.0071715
M3 - Article
C2 - 23977123
AN - SCOPUS:84882631243
SN - 1932-6203
VL - 8
JO - PLoS ONE
JF - PLoS ONE
IS - 8
M1 - e71715
ER -