Detection of compound structures using clustering of statistical and structural features

We describe a new method for detecting compound structures in images by combining the statistical and structural characteristics of simple primitive objects. A graph is constructed by assigning the primitive objects to its vertices, and connecting potentially related objects using edges. Statistical information that is modeled using spectral, shape, and position data of individual objects as well as the structural information that is modeled in terms of spatial alignments of neighboring object groups are also encoded in this graph. Experiments using WorldView-2 data show that hierarchical clustering of the graph vertices can discover high-level compound structures that cannot be obtained using traditional techniques.