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MRFs

MRFs.  (X 1 ,X 2 ). 1. 2. 3. X 3. X 1. X 2. 4.  (X 2 ,X 3 ,X 3 ). X 4. CRFs. Image I.  (X 1 ,X 2 ,I). 1. 2. 3. X 3. X 1. X 2. 4.  (X 2 ,X 3 ,X 3 ,I). X 4. CRF. MRF. Examples. X = Image patches. [Quattoni et al. ]. X = Patches on a regular lattice. [Kumar].

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MRFs

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  1. MRFs (X1,X2) 1 2 3 X3 X1 X2 4 (X2,X3,X3) X4

  2. CRFs Image I (X1,X2,I) 1 2 3 X3 X1 X2 4 (X2,X3,X3,I) X4

  3. CRF MRF Examples X = Image patches [Quattoni et al.] X = Patches on a regular lattice [Kumar]

  4. Examples X = pixels, regions, image [He et al.]

  5. Issues • Inference • Easy only when the planets are aligned • Approximate solutions only  How good are they? • Learning • Difficult and slow • Limits the complexity of the models

  6. Issues • Generality • Can use arbitrary models but limited to restricted models in practice because of inference and learning challenges • Global label inference • Inference over global labeling of the data in theory, but limited propagation across image in practice • Support limited by the complexity of learning and inference • Use of complex graph and clique structure is difficult

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