A New Approach To Graph Based Object Classification On Images

The main idea of graph based image modeling is that the regions of the image, which contain similar properties, are denoted by graph vertices, and the relations between different regions are denoted by graph edges. The vertex and edge attributes usually describe the characteristics of that region and the relation between regions respectively. A simple approach to keep the structural and topological information of an image is to use digital image representation techniques; for instance, quad trees, etc. By modeling images as graphs, the task of image classification becomes one of classifying graphs. Quad trees have been used for representing images in the form of trees. In this paper we propose an algorithm that discovers the subgraphs present in a graph represented by a stream of labeled nodes and edges.Our algorithm is efficient and is easily tuned by the user to produce interesting patterns from various kinds of graph data.