We propose an approach to include contextual features for labeling images, in which each pixel is assigned to one of a finite set of labels. The features are incorporated into a probabilistic framework, which combines the outputs of several components. Components differ in the information they encode. Some focus on the image-label mapping, while others focus solely on patterns within the label field. Components also differ in their scale, as some focus on fine-resolution patterns while others on coarser, more global structure. A supervised version of the contrastive divergence algorithm is applied to learn these features from labeled image data. We demonstrate performance on two real-world image databases and compare it to a classifier and a Markov random field.
Multiscale conditional random fields for image labeling
2004-01-01
455689 byte
Conference paper
Electronic Resource
English
Multiscale Conditional Random Fields for Image Labeling
British Library Conference Proceedings | 2004
|Curb reconstruction using Conditional Random Fields
IEEE | 2010
|On Learning Conditional Random Fields for Stereo
British Library Online Contents | 2012
|