Using Multiple Segmentations to Discover Objects and their Extent in Image Collections

  • Authors:
  • Bryan C. Russell;William T. Freeman;Alexei A. Efros;Josef Sivic;Andrew Zisserman

  • Affiliations:
  • MIT;MIT;Carnegie Mellon University;University of Oxford, UK;University of Oxford, UK

  • Venue:
  • CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. To tie these ideas together we compute multiple segmentations of each image and then: (i) learn the object classes; and (ii) choose the correct segmentations. We demonstrate that such an algorithm succeeds in automatically discovering many familiar objects in a variety of image datasets, including those from Caltech, MSRC and LabelMe.