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Learning High-Order Structures for Texture Retrieval

  • Ni LiuEmail author
  • Georgy Gimel’farb
  • Patrice Delmas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9069)

Abstract

Learning interaction structures of graphical models, such as a high-order Markov-Gibbs random field (MGRF), is an open challenge. For a translation and contrast/offset invariant MGRF model of image texture, we sequentially construct higher-order cliques from the lower-order ones, starting from characteristic 2nd-order interactions (i.e. edges of the interaction graph). Every next-order clique is built by adding the available required edges to one of the current cliques. Experiments on texture databases resulted in the interpretable and intuitive learned cliques of up to 20th order. The learned high-order cliques consistently outperform in texture retrieval the multiple 2nd-order ones and the state-of-the-art local binary (LBP) and ternary patterns (LTP).

Keywords

Training Image Local Binary Pattern Markov Chain Model Interaction Graph Texture Retrieval 
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 Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of AucklandAucklandNew Zealand

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