We introduce a new machine learning approach for image segmentation, based on a joint energy model over image features and novel local binary shape descriptors. These descriptors compactly represent rich shape information at multiple scales, including interactions between multiple objects. Our approach, which does not rely on any hand-designed features, reflects the inherent combinatorial nature of dense image segmentation problems. We propose efficient algorithms for learning deep neural networks to model the joint energy, and for local optimization of this energy in the space of supervoxel agglomerations. We demonstrate the effectiveness of our approach on 3-D biological data, where rich shape information appears to be critical for resolving ambiguities. On two challenging 3-D electron microscopy datasets highly relevant to ongoing efforts towards large-scale dense mapping of neural circuits, we achieve state-of-the-art segmentation accuracy.

本文提出了一种基于神经网络的图像分割方法（CELIS），其能够处理密集图像分割问题并有效地进行3D数据的体积重建，该方法在公共可获得的3D显微镜数据上的测试结果表明，相较于基于图形分割的方法和随机森林聚合方法提高了高达20%的体积重建准确度。

组合能量学习在图像分割中的应用