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Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
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| Venue: | CVPR |
| Citations: | 315 - 26 self |
BibTeX
@ARTICLE{Russell_usingmultiple,
author = {Bryan C. Russell and Alexei A. Efros and Josef Sivic and William T. Freeman and Andrew Zisserman},
title = {Using Multiple Segmentations to Discover Objects and their Extent in Image Collections},
journal = {CVPR},
year = {}
}
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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. 1.
