Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/84361
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Conference paper |
Title: | Weakly supervised top-down image segmentation |
Author: | Vasconcelos, M. Carneiro, G. Vasconcelos, N. |
Citation: | 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition: volume 2, 2006 / A. Fitzgibbon, C. J. Taylor, Y. LeCun (eds.), pp.1001-1006 |
Publisher: | IEEE Computer Society |
Publisher Place: | USA |
Issue Date: | 2006 |
ISBN: | 0769525970 9780769525976 |
ISSN: | 1063-6919 |
Conference Name: | IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006) (2006 : New York, USA) |
Statement of Responsibility: | Manuela Vasconcelos, Gustavo Carneiro, Nuno Vasconcelos |
Abstract: | There has recently been significant interest in top-down image segmentation methods, which incorporate the recognition of visual concepts as an intermediate step of segmentation. This work addresses the problem of top-down segmentation with weak supervision. Under this framework, learning does not require a set of manually segmented examples for each concept of interest, but simply a weakly labeled training set. This is a training set where images are annotated with a set of keywords describing their contents, but visual concepts are not explicitly segmented and no correspondence is specified between keywords and image regions. We demonstrate, both analytically and empirically, that weakly supervised segmentation is feasible when certain conditions hold. We also propose a simple weakly supervised segmentation algorithm that extends state-of-theart bottom-up segmentation methods in the direction of perceptually meaningful segmentation1. |
Rights: | Copyright © 2006 by The Institute of Electrical and Electronics Engineers, Inc. |
DOI: | 10.1109/CVPR.2006.333 |
Published version: | http://dx.doi.org/10.1109/cvpr.2006.333 |
Appears in Collections: | Aurora harvest Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.