The MIST methodology and its application to natural scene interpretation

Ryszard S. Michalski, Qi Zhang, Marcus A. Maloof, and Eric Bloedorn

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 results illustrate the performance of the learning programs for the chosen problem of natural scene interpretation in terms of predictive accuracy, training time, recognition time, and complexity of the induced descriptions. The MIST methodology has proven to be very useful for the presented application. Overall, the experiments performed indicate that the multistrategy learning program AQ-NN appears to be the most promising approach.

Paper available in PostScript (gzipped) and PDF.

Michalski, R.S., Zhang, Q., Maloof, M.A., & Bloedorn, E. (1996). The MIST methodology and its application to natural scene interpretation. Proceedings of the Image Understanding Workshop, 1473–1479. San Francisco, CA: Morgan Kaufmann.

@inproceedings{michalski.iuw.96b,
  author = "Michalski, R.S. and Zhang, Q. and Maloof, M.A. and Bloedorn, E.",
  title = "The {MIST} methodology and its application to natural scene
    interpretation",
  booktitle = "{Proceedings of the Image Understanding Workshop}",
  year = 1996,
  pages = "1473--1479",
  address = "San Francisco, CA",
  publisher = "Morgan Kaufmann"
}