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.
|Title of host publication||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Publisher||IEEE Computer Society|
|Number of pages||8|
|ISBN (Electronic)||9781479951178, 9781479951178|
|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
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Other||27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014|
|Period||14/6/23 → 14/6/28|
Bibliographical notePublisher Copyright:
© 2014 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition