Modelling Objects using Distribution and Topology of Multiscale Region Pairs

We propose a method for simultaneous detection, localization and segmentation of objects of a known category. We show that this is possible by using segments as features. To this end, we propose an object model in which the image is represented as a tree, that captures containment relationships among the segments. Using segments as features has the advantage that object detection and segmentation is done simultaneously, forgoing the need for a separate sophisticated model for object segmentation. A generative model of an object category is estimated in a supervised mode, in terms of the characteristics of its constituent regions, their relative locations, and their mutual containment. The novel aspect of this work lies in simplifying the description of the hierarchy in terms of constraints that apply to only pairs of nodes, instead of all nodes in the tree. We show that this indeed improves the speed of learning algorithm. Inference is done using graph cuts. We report the performance of the model on standard datasets.