Chapter

Pattern Recognition

Volume 6835 of the series Lecture Notes in Computer Science pp 111-121

Putting MAP Back on the Map

  • Patrick PletscherAffiliated withLancaster UniversityETH Zurich
  • , Sebastian NowozinAffiliated withLancaster UniversityMicrosoft Research Cambridge
  • , Pushmeet KohliAffiliated withLancaster UniversityMicrosoft Research Cambridge
  • , Carsten RotherAffiliated withLancaster UniversityMicrosoft Research Cambridge

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Abstract

Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics.