Abstract
We present Max-Margin Boltzmann Machines (MMBMs) for object segmentation. MMBMs are essentially a class of Conditional Boltzmann Machines that model the joint distribution of hidden variables and output labels conditioned on input observations. In addition to image-to-label connections, we build direct image-to-hidden connections to facilitate global shape prediction, and thus derive a simple Iterated Conditional Modes algorithm for efficient maximum a posteriori inference. We formulate a max-margin objective function for discriminative training, and analyze the effects of different margin functions on learning. We evaluate MMBMs using three datasets against state-of-the-art methods to demonstrate the strength of the proposed algorithms.
Original language | English |
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Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Publisher | IEEE Computer Society |
Pages | 320-327 |
Number of pages | 8 |
ISBN (Electronic) | 9781479951178, 9781479951178 |
DOIs | |
Publication status | Published - 2014 Sep 24 |
Event | 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States Duration: 2014 Jun 23 → 2014 Jun 28 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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ISSN (Print) | 1063-6919 |
Other
Other | 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 |
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Country/Territory | United States |
City | Columbus |
Period | 14/6/23 → 14/6/28 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition