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Proceedings of the Seventh European Conference on Computer Vision, 2002. Learning a Sparse Representation for Object Detection
 

Summary: Proceedings of the Seventh European Conference on Computer Vision, 2002.
Learning a Sparse Representation for Object Detection
Shivani Agarwal and Dan Roth
Department of Computer Science
University of Illinois at Urbana-Champaign
Urbana, IL 61801, USA
sagarwal,danrĄ @cs.uiuc.edu
Abstract. We present an approach for learning to detect objects in still gray im-
ages, that is based on a sparse, part-based representation of objects. A vocabulary
of information-rich object parts is automatically constructed from a set of sam-
ple images of the object class of interest. Images are then represented using parts
from this vocabulary, along with spatial relations observed among them. Based
on this representation, a feature-efficient learning algorithm is used to learn to de-
tect instances of the object class. The framework developed can be applied to any
object with distinguishable parts in a relatively fixed spatial configuration. We
report experiments on images of side views of cars. Our experiments show that
the method achieves high detection accuracy on a difficult test set of real-world
images, and is highly robust to partial occlusion and background variation.
In addition, we discuss and offer solutions to several methodological issues that
are significant for the research community to be able to evaluate object detection

  

Source: Agarwal, Shivani - Department of Computer Science and Automation, Indian Institute of Science, Bangalore

 

Collections: Computer Technologies and Information Sciences