In this chapter, we aim to connect the areas of object categorization and figure-ground segmentation. We present a novel method for the categorization of unfamiliar objects in difficult real-world scenes. The method generates object hypotheses without prior segmentation, which in turn can be used to obtain a category-specific figure-ground segmentation. In particular, the proposed approach uses a probabilistic formulation to incorporate knowledge about the recognized category as well as the supporting information in the image to segment the object from the background. This segmentation can then be used for hypothesis verification, to further improve recognition performance. Experimental results show the capacity of the approach to categorize and segment object categories as diverse as cars and cows.
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