Hayit K. Greenspan, Rodney Goodman, Rama Chellappa
A combined neural network and rule-based approach is suggested as a general framework for pattern recognition. This approach enables unsu(cid:173) pervised and supervised learning, respectively, while providing probability estimates for the output classes. The probability maps are utilized for higher level analysis such as a feedback for smoothing over the output la(cid:173) bel maps and the identification of unknown patterns (pattern "discovery"). The suggested approach is presented and demonstrated in the texture - analysis task. A correct classification rate in the 90 percentile is achieved for both unstructured and structured natural texture mosaics. The advan(cid:173) tages of the probabilistic approach to pattern analysis are demonstrated.