Learning descriptions of 2D shapes for object recognition in X-ray images

Marcus A. Maloof and Ryszard S. Michalski

This paper describes a method for learning shape descriptions of 2D objects in x-ray images. The descriptions are induced from shape examples using the AQ15c inductive learning system. The method has been experimentally compared to k-nearest neighbor, a statistical pattern recognition technique, and artificial neural networks. Experimental results demonstrate strong advantages of the AQ methodology over the other methods. Specifically, the method has higher predictive accuracy and faster learning and recognition rates. The application considered is detecting blasting caps in x-ray images of luggage. An intelligent system performing this detection task can be used to assist airport security personnel with luggage screening.

Paper available in PostScript (gzipped) and PDF.

Maloof, M.A., & Michalski, R.S. (1995). Learning descriptions of 2D shapes for object recognition in X-ray images. Proceedings of the Eighth International Symposium on Artificial Intelligence, 124–131. Monterrey, Mexico: ITESM.

@inproceedings{maloof.isai.95,
  author = "Maloof, M.A. and Michalski, R.S.",
  title = "Learning descriptions of {2D} shapes for object recognition
    in {X}-ray images",
  booktitle = "{Proceedings of the Eighth International Symposium
    on Artificial Intelligence}",
  year = 1995,
  pages = "124--131",
  publisher = "ITESM",
  address = "Monterrey, Mexico"
}