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

Marcus A. Maloof and Ryszard S. Michalski

This paper describes a method for applying AQ15c to learning shape descriptions of 2D blob-like objects in X-ray images. The methodology and initial experimental results are discussed, along with comparisons to k-nearest neighbor and to feed-forward neural networks. The AQ15c learning method is shown to have distinct advantages over the aforementioned techniques in terms of higher or comprable classification accuracy, learning and recognition time, and understandability of learned concepts. This approach is well-suited for recognizing objects that can be isolated in the image using histogram and thresholding techniques and that have little internal structure.

Paper available in PostScript (gzipped) and PDF.

Maloof, M.A., & Michalski, R.S. (1994). Learning descriptions of 2D blob-like shapes for object recognition in X-ray images: an initial study. Reports of the Machine Learning and Inference Laboratory, MLI 94-4. Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA.

@techreport{maloof.tr.94,
  author = "Maloof, M.A. and Michalski, R.S.",
  title = "Learning descriptions of {2D} blob-like shapes for object 
    recognition in {X-ray} images: An initial study",
  type = "{Reports of the Machine Learning and Inference Laboratory}",
  number = "MLI 94-4",
  year = 1994,
  institution = "Machine Learning and Inference Laboratory, George
    Mason University",
  address = "Fairfax, VA"
}