This work addresses the problem of automatic classification and labeling of 19th- and 20th-century quilts from
photographs. The photographs are classified according to the quilt patterns into crazy and non - crazy categories. Based
on the classification labels, humanists try to understand the distinct characteristics of an individual quilt-maker or
relevant quilt-making groups in terms of their choices of pattern selection, color choices, layout, and original deviations
from traditional patterns. While manual assignment of crazy and non-crazy labels can be achieved by visual inspection,
there does not currently exist a clear definition of the level of crazy-ness, nor an automated method for classifying
patterns as crazy and non-crazy.
We approach the problem by modeling the level of crazy-ness by the distribution of clusters of color-homogeneous
connected image segments of similar shapes. First, we extract signatures (a set of features) of quilt images that represent
our model of crazy-ness. Next, we use a supervised classification method, such as the Support Vector Machine (SVM)
with the radial basis function, to train and test the SVM model. Finally, the SVM model is optimized using N-fold cross
validation and the classification accuracy is reported over a set of 39 quilt images.