5 research outputs found
A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation
We propose a novel theoretical framework that generalizes algorithms for
hierarchical agglomerative clustering to weighted graphs with both attractive
and repulsive interactions between the nodes. This framework defines GASP, a
Generalized Algorithm for Signed graph Partitioning, and allows us to explore
many combinations of different linkage criteria and cannot-link constraints. We
prove the equivalence of existing clustering methods to some of those
combinations, and introduce new algorithms for combinations which have not been
studied. An extensive comparison is performed to evaluate properties of the
clustering algorithms in the context of instance segmentation in images,
including robustness to noise and efficiency. We show how one of the new
algorithms proposed in our framework outperforms all previously known
agglomerative methods for signed graphs, both on the competitive CREMI 2016 EM
segmentation benchmark and on the CityScapes dataset.Comment: 19 pages, 8 figures, 6 table
Machine Learning for Instance Segmentation
Volumetric Electron Microscopy images can be used for connectomics, the study of brain connectivity at the cellular level.
A prerequisite for this inquiry is the automatic identification of neural cells, which requires machine learning algorithms and in particular efficient image segmentation algorithms.
In this thesis, we develop new algorithms for this task.
In the first part we provide, for the first time in this
field, a method for training a neural network to predict optimal input data for a watershed algorithm.
We demonstrate its superior performance compared to other segmentation methods of its category.
In the second part, we develop an efficient watershed-based algorithm for weighted graph
partitioning, the \emph{Mutex Watershed}, which uses negative edge-weights for the first time.
We show that it is intimately related to the multicut and has a cutting edge performance on a connectomics challenge.
Our algorithm is currently used by the leaders of two connectomics challenges.
Finally, motivated by inpainting neural networks, we create a method to learn the graph weights without any supervision