A Hierarchical Field Framework for Unified Context-Based Classification

Sanjiv Kumar and Martial Hebert
Tenth IEEE International Conference on Computer Vision (ICCV '05), October, 2005, pp. 1284 - 1291.


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Abstract
We present a two-layer hierarchical formulation to exploit different levels of contextual information in images for robust classification. Each layer is modeled as a conditional field that allows one to capture arbitrary observation-dependent label interactions. The proposed framework has two main advantages. First, it encodes both the short-range interactions (e.g., pixelwise label smoothing) as well as the long-range interactions (e.g., relative configurations of objects or regions) in a tractable manner. Second, the formulation is general enough to be applied to different domains ranging from pixelwise image labeling to contextual object detection. The parameters of the model are learned using a sequential maximum-likelihood approximation. The benefits of the proposed framework are demonstrated on four different datasets and comparison results are presented.

Keywords
vision

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Number of pages: 8

Text Reference
Sanjiv Kumar and Martial Hebert, "A Hierarchical Field Framework for Unified Context-Based Classification," Tenth IEEE International Conference on Computer Vision (ICCV '05), October, 2005, pp. 1284 - 1291.

BibTeX Reference
@inproceedings{Kumar_2005_5291,
   author = "Sanjiv Kumar and Martial Hebert",
   editor = "IEEE",
   title = "A Hierarchical Field Framework for Unified Context-Based Classification",
   booktitle = "Tenth IEEE International Conference on Computer Vision (ICCV '05)",
   pages = "1284 - 1291",
   month = "October",
   year = "2005",
   volume = "2",
}