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RI | Publications | Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
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Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
B.C. Russell, A.A. Efros, J. Sivic, W.T. Freeman, and A. Zisserman
Proceedings of CVPR, June, 2006.
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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.
| Notes |
Number of pages: 8
| Text Reference |
B.C. Russell, A.A. Efros, J. Sivic, W.T. Freeman, and A. Zisserman, "Using Multiple Segmentations to Discover Objects and their Extent in Image Collections," Proceedings of CVPR, June, 2006.
| BibTeX Reference |
@inproceedings{Russell_2006_5395,
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",
booktitle = "Proceedings of CVPR",
month = "June",
year = "2006"
}