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Markov–Gibbs Texture Modelling with Learnt Freeform Filters

  • Ralph VersteegenEmail author
  • Georgy Gimel’farb
  • Patricia Riddle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

Energy-based Markov–Gibbs random field (MGRF) image models describe images by statistics of localised features; hence selecting statistics is crucial. This paper presently a procedure for searching much broader than typical families of linear-filter-based statistics, by alternately optimising in continuous parameter space and discrete graphical structure space. This unifies and extends the divergent models deriving from the well-known Fields of Experts (FoE), which learn parametrised features built on small linear filters, and the constrasting FRAME (exponential family) approach which iteratively selects large filters from a fixed set. While FoE is limited by computational cost to small filters, we use large sparse (non-contiguous) filters with arbitrary shapes which can capture long-range interactions directly. A filter pre-training step also improves speed and results. Synthesis of a variety of textures shows promising abilities of the proposed models to capture both fine details and larger-scale structure with a low number of small and efficient filters.

Keywords

Local Binary Pattern Gibbs Sampling Nest Iteration Texture Synthesis Texture Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ralph Versteegen
    • 1
    Email author
  • Georgy Gimel’farb
    • 1
  • Patricia Riddle
    • 1
  1. 1.Department of Computer ScienceThe University of AucklandAucklandNew Zealand

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