Representation of Edge Detection Results Based on Graph Theory

This paper describes a concept of image retrieval method based on graph theory, used to speed up the process of edge detection and to represent results in more efficient way. We assume that result representation of edge detection based on graph theory is more efficient than standard map-based representation. Advantages of graph-based representation are direct access to edge nodes of the shape without search and segmentation of edges points as is the case with map-based representations. Another advance is less data consumption, only data for nodes and their connections are needed, what is important in large database applications for good scalability. In the described approach we reduce the amount of necessary image data to examine by modifying some standard edge detection method. To obtain that, we use an auxiliary grid to detect points of edge intersections with grid lines. Each intersection point becomes a node of graph that is a base element of the graph-based representation. Finally, our method based on edge segmentation creates connections between graph nodes determined in the previous steps of the algorithm. The method analyzes an image independently in squares determined by an auxiliary grid, which can be fork and parallel processed. We motivate the idea of our work that it will be used to develop a method for image feature extraction in CBIR for database applications.