We propose a new object representation, called connected segmentation tree (CST), which captures canonical characteristics of the object in terms of the photometric, geometric, and spatial adjacency and containment properties of its constituent image regions. CST is obtained by augmenting the object’s segmentation tree (ST) with inter-region neighbor links, in addition to their recursive embedding structure already present in ST. This makes CST a hierarchy of region adjacency graphs. A region’s neighbors are computed using an extension to regions of the Voronoi diagram for point patterns. Unsupervised learning of the CST model of a category is formulated as matching the CST graph representations of unlabeled training images, and fusing their maximally matching subgraphs.

• N. Ahuja and S. Todorovic, Connected Segmentation Tree — A Joint Representation of Region Layout and Hierarchy, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, June 2008.