Minimax Entropy Principle and Its Application to Texture Modeling

@article{Zhu1997MinimaxEP,
  title={Minimax Entropy Principle and Its Application to Texture Modeling},
  author={Song-Chun Zhu and Ying Nian Wu and David Mumford},
  journal={Neural Computation},
  year={1997},
  volume={9},
  pages={1627-1660}
}
This article proposes a general theory and methodology, called the minimax entropy principle, for building statistical models for images (or signals) in a variety of applications. This principle consists of two parts. The first is the maximum entropy principle for feature binding (or fusion): for a given set of observed feature statistics, a distribution can be built to bind these feature statistics together by maximizing the entropy over all distributions that reproduce them. The second part… CONTINUE READING
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