Publications

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication


Year(s) from:  to 
Author:
Keywords (separated by spaces):

Combined Object Categorization and Segmentation with an Implicit Shape Model

B. Leibe, A. Leonardis, and B. Schiele
ECCV'04 Workshop on Statistical Learning in Computer Vision
Prague, Czech Republic, May 2004

Abstract

We present a method for object categorization in real-world scenes. Following a common consensus in the field, we do not assume that a figureground 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 into a common probabilistic framework. 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 between overlapping hypotheses with a novel MDL-based criterion. In addition, we present an extensive evaluation of our method on a standard dataset for car detection and compare its performance to existing methods from the literature. Our results show that the proposed method significantly outperforms previously published methods while needing one order of magnitude less training examples. Finally, we present results for articulated objects, which show that the proposed method can categorize and segment unfamiliar objects in different articulations and with widely varying texture patterns, even under significant partial occlusion.


Download in pdf format
@InProceedings{eth_biwi_00411,
  author = {B. Leibe and A. Leonardis and and B. Schiele},
  title = {Combined Object Categorization and Segmentation with an Implicit Shape Model},
  booktitle = {ECCV'04 Workshop on Statistical Learning in Computer Vision},
  year = {2004},
  month = {May},
  keywords = {}
}