Combinatorial Energy Learning for Image Segmentation

Understand

We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image.

• Our approach, combinatorial energy learning for image segmentation (CELIS) places a particular emphasis on modeling the inherent combinatorial nature of dense image segmentation problems.
• We propose efficient algorithms for learning deep neural networks to model the energy function, and for local optimization of this energy in the space of supervoxel agglomerations.
• We extensively evaluate our method on a publicly available 3-D microscopy dataset with 25 billion voxels of ground truth data.