The MIST Methodology and Its Application to Natural Scene Interpretation

@inproceedings{Michalski1999TheMM,
  title={The MIST Methodology and Its Application to Natural Scene Interpretation},
  author={Ryszard S. Michalski and Qinlong Zhang and Marcus A. Maloof and Eric Bloedorn},
  year={1999}
}
The MIST methodology (Multi-level Image Sampling and Transformation) provides an environment for applying diverse machine learning methods to problems of computer vision. The methodology is illustrated by a problem of learning how to conceptually interpret natural scenes. In the experiments described, three learning programs were used: AQ15c—for learning decision rules from examples, NN—neural net learning, and AQNN—multistrategy learning combining symbolic and neural net methods. Presented… CONTINUE READING