Piecewise Flat Embedding for Image Segmentation
Piecewise Flat Embedding for Image Segmentation 第一階段：對正交性約束項（也即是我們前面公式中的r值）採用較大的懲罰係數。外環中的迭代使嵌入數據的不同維數彼此正交，以消除它們之間的冗餘。 第二階段：這一階段只在內部循環中執行Bregman迭代以最小化l1正則化的目標函數，而不需要在外部循環中執行SVD來嚴格執行正交性。這種正交約束的鬆弛使得l1正則化
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相關文章
- 1. Attention Guided Network for Retinal Image Segmentation
- 2. embedding 和segmentation representation
- 3. FEELVOS：Fast End-to-End Embedding Learning for Video Object Segmentation
- 4. Image saliency_detection / segmentation論文
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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