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Combined Object Categorization and Segmentation With An Implicit Shape Model (2004)
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| Venue: | In ECCV workshop on statistical learning in computer vision |
| Citations: | 405 - 10 self |
BibTeX
@INPROCEEDINGS{Leibe04combinedobject,
author = {Bastian Leibe and Ales Leonardis and Bernt Schiele},
title = {Combined Object Categorization and Segmentation With An Implicit Shape Model},
booktitle = {In ECCV workshop on statistical learning in computer vision},
year = {2004},
pages = {17--32}
}
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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 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. 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.
Keyphrases
object categorization implicit shape model different articulation real-world scene segmentation result object class recognition per-pixel confidence measure figure-ground segmentation extensive evaluation multiple object novel mdl-based criterion common probabilistic framework segment unfamiliar object standard approach significant partial occlusion standard dataset training example texture pattern common consensus natural extension articulated object car detection
