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Semi-iterative Inferences with Hierarchical Energy-Based Models for Image Analysis

  • Annabelle Chardin
  • Patrick Pérez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1654)

Abstract

This paper deals with hierarchical Markov Random Field models. We propose to introduce newhi erarchical models based on a hybrid structure which combines a spatial grid of a reduced size at the coarsest level with sub-trees appended below it, down to the finest level. These models circumvent the algorithmic drawbacks of grid-based models (computational load and/or great dependance on the initialization) and the modeling drawbacks of tree-based approaches (cumbersome and somehowa rtificial structure). The hybrid structure leads to algorithms that mix a non-iterative inference on sub-trees with an iterative deterministic inference at the top of the structure. Experiments on synthetic images demonstrate the gains provided in terms of both computational efficiency and quality of results. Then experiments on real satellite spot images illustrate the ability of hybrid models to perform efficiently the multispectral image analysis.

Keywords

Hierarchical Model Hybrid Structure Coarse Level Synthetic Image Multigrid Approach 
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-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Annabelle Chardin
    • 1
  • Patrick Pérez
    • 1
  1. 1.IRISA/INRIACampus de BeaulieuRennes cedexFrance

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