An Implicit Shape Model for Combined Object Categorization and Segmentation

We present a method for object categorization in real-world scenes. Following a common consensus in the field, we do not assume that a figure-ground segmentation is available prior to recognition. However, in contrast to most standard approaches for object class recognition, our approach automatically segments the object as a result of the categorization. This combination of recognition and segmentation into one process is made possible by our use of an Implicit Shape Model, which integrates both capabilities into a common probabilistic framework. This model can be thought of as a non-parametric approach which can easily handle configurations of large numbers of object parts. In addition to the recognition and segmentation result, it also generates a per-pixel confidence measure specifying the area that supports a hypothesis and how much it can be trusted. We use this confidence to derive a natural extension of the approach to handle multiple objects in a scene and resolve ambiguities ...

• Type: Conference
• Year: 2006
• Where: CLOR
• Authors: Bastian Leibe, Ales Leonardis, Bernt Schiele
• Keywords: CLOR 2006, Computer Vision, Figure-ground Segmentation, Object Categorization, Object Class Recognition