Feature utilization of continuously varying attributes in visual pattern classification

Feature utilization of continuously varying attributes was examined in the context of classification of random polygons, the collection of which contained no a priori classes based upon physical features. Ten Os sorted 60 eight-sided patterns into two through nine groups. One week later, each O repeated three sorts and then placed 30 new patterns into the groups of his previous five-group sort. Comparison of groups across sessions primarily showed repetition of initial classification. Stepwise discriminant analyses were performed on Os' classes, predictors being physical measures of various pattern attributes. Significant predictors of all classifications were found, the two most prevalent predictor types being measures of compactness and jaggedness. LDFs based on original pattern classes showed