Possibilistic Clustering of Generic Shapes Derived from Templates

Abstract

We present in this paper a new type of alternating-optimization based possibilistic c-shell algorithm for clustering template-based shapes. A cluster prototype consists of a copy of the template after translation, seating, rotation, and/or affine transformations. We use a number of two-dimensional data sets, both synthetic and from real-world images, to illustrate the capability of our algorithm in detecting generic template-based shapes in images. We also describe a progressive clustering procedure aimed to relax the requirements of known number of dusters and good initialization.