Abstract While Markov Random Fields (MRFs) are widely used in computer vision, they
present a quite challenging inference problem. MRF inference can be accelerated by pre-
processing techniques like Dead End Elimination (DEE) or QPBO-based approaches which
compute the optimal labeling of a subset of variables. These techniques are guaranteed to
never wrongly label a variable but they often leave a large number of variables unlabeled.
We address this shortcoming by interpreting pre-processing as a classification problem …

We will give a detailed running time analysis of our proposed algorithm in Section 2. Then
we will give the proof to Lemma 4 and Theorem 10 in Section 3 and Section 4 respectively.
Generalization of the efficient discriminative criterion check subroutine will be described in
Section 5. More implementation details will be given in Section 6. Finally, we will provide
more experimental data in Section 7, including visualization results, experimental results on
a typical parameter setup, more investigation on parameters sensitivity, the role of worst …