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|Title:||Weakly supervised top-down image segmentation|
|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|
|Conference Name:||IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006) (2006 : New York, USA)|
|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.|
|Appears in Collections:||Aurora harvest|
Computer Science publications
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