Possibilistic c-template clustering and its application in object detection in images

Abstract

We present in this paper a new type of alternating-optimization based possibilistic c-shell clustering algorithm called possibilistic c-template (PCT). A template is represented by a set of line segments. A cluster prototype consists of a copy of the template after translation, scaling, and rotation transforms. This extends the capability of shell clustering beyond a few standard geometrical shapes that have been studied so far. We use a number of 2-dimensional data sets to illustrate the application of our algorithm in detecting generic template-based shapes in images. Techniques taken to relax the requirements of known number of clusters and good initialization are also described. Results for both synthetic and actual image data are presented.