Skeletonization and classification by Bayesian classifier algorithm for object recognition

This paper describes and demonstrates a graphical method of Skeletal Shock Graph, which is based on the shape or geometry of the object. Shock Graph is an abstraction of the skeleton of a shape onto a Directed Acyclic Graph (DAG) in which the skeleton points are labeled according to the local variation of the radius function at each point. A large image data base is created by using suitable image acquisition technique, which is converted into binary images. Skeleton and its labeling of the binary image is obtained by applying Skeletonization Algorithm. Then next steps adopted are formation of Shock Graph and labeled tree, indexing the data base and generation of attribute vectors, pruning the data base and lastly matching the tree of query image with that of database images for recognition. This paper discusses and demonstrates the existing challenges and prospective research areas in Skeletal Shock Graph based object recognition and also presents some comparative results against the geodesic path method.