Region and constellations based categorization of images with unsupervised graph learning

In this paper, we address the problem of image categorization with a fast novel method based on the unsupervised clustering of graphs in the context of both region-based segmentation and the constellation approach to object recognition. Such method is an EM central clustering algorithm which builds prototypical graphs on the basis of either Softassign or fast matching with graph transformations. We present two realistic applications and their experimental results: categorization of image segmentations and visual localization. We compare our graph prototypes with the set median graphs. Our results reveal that, on the one hand, structure extracted from images improves appearance-based visual localization accuracy. On the other hand, we show that the cost of our central graph clustering algorithm is the cost of a pairwise algorithm. We also discuss how the method scales with an increasing amount of images. In addition, we address the scientific question of what are the bounds of structural learning for categorization. Our in-depth experiments both for region-based and feature-based image categorization, will show that such bounds depend hardly on structural variability.