Two Simple Shock Graph Structures for Object Recognition Using Euler String

The object recognition framework presented here converts every image from the database to the binary format which is then converted to skeleton also called as Medial axis transform (MAT). The skeleton is obtained using morphological operations. The skeleton is converted to a shock graph which has structure like a tree. Shock graphs are derived from the skeleton and have emerged as powerful 2-D shape representation method. A skeleton has number of branches. A branch is a connected set of points between an end point and a joint or another end point. Every point also called as shock point on a skeleton can be labeled according to the variation of the radius function. The labeled points in a given branch are to be grouped according to their labels and connectivity, so that each group of connected points having the same label will be stored in a graph node. One skeleton branch can give rise to one or more nodes. Finally edges are added between the nodes to produce a directed acyclic graph. The task of object recognition is performed by comparing the graph structures of different images. Euler string was generated for each graph and the string representation of each graph was matched with the graph of the query object. Two different graph structures have been proposed called as conventional and improved graph. Two types of graphs are generated for each object and object recognition performance is evaluated for both types of graphs by evaluating the time taken by each graph type for recognition and in terms of matching efficiency. Both graph structures are simple and produce satisfactory matching results on binary objects.