European Conference on Computer Vision

ECCV 2008: Computer Vision – ECCV 2008 pp 481-494

Object Recognition by Integrating Multiple Image Segmentations

  • Caroline Pantofaru
  • Cordelia Schmid
  • Martial Hebert
Conference paper

DOI: 10.1007/978-3-540-88690-7_36

Volume 5304 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Pantofaru C., Schmid C., Hebert M. (2008) Object Recognition by Integrating Multiple Image Segmentations. In: Forsyth D., Torr P., Zisserman A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5304. Springer, Berlin, Heidelberg

Abstract

The joint tasks of object recognition and object segmentation from a single image are complex in their requirement of not only correct classification, but also deciding exactly which pixels belong to the object. Exploring all possible pixel subsets is prohibitively expensive, leading to recent approaches which use unsupervised image segmentation to reduce the size of the configuration space. Image segmentation, however, is known to be unstable, strongly affected by small image perturbations, feature choices, or different segmentation algorithms. This instability has led to advocacy for using multiple segmentations of an image. In this paper, we explore the question of how to best integrate the information from multiple bottom-up segmentations of an image to improve object recognition robustness. By integrating the image partition hypotheses in an intuitive combined top-down and bottom-up recognition approach, we improve object and feature support. We further explore possible extensions of our method and whether they provide improved performance. Results are presented on the MSRC 21-class data set and the Pascal VOC2007 object segmentation challenge.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Caroline Pantofaru
    • 1
  • Cordelia Schmid
    • 2
  • Martial Hebert
    • 1
  1. 1.The Robotics InstituteCarnegie Mellon UniversityUSA
  2. 2.INRIA Grenoble, LEAR, LJKFrance