Piecewise Flat Embedding for Image Segmentation
Piecewise Flat Embedding for Image Segmentation 第一阶段：对正交性约束项（也即是我们前面公式中的r值）采用较大的惩罚系数。外环中的迭代使嵌入数据的不同维数彼此正交，以消除它们之间的冗余。 第二阶段：这一阶段只在内部循环中执行Bregman迭代以最小化l1正则化的目标函数，而不需要在外部循环中执行SVD来严格执行正交性。这种正交约束的松弛使得l1正则化
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相关文章
- 1. Attention Guided Network for Retinal Image Segmentation
- 2. embedding 和segmentation representation
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- 9. Paper | U-Net: Convolutional Networks for Biomedical Image Segmentation
- 10. ICNet for Real-Time Semantic Segmentation on High-Resolution Image