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A Hierarchical Field Framework for Unified Context-Based Classification

Sanjiv Kumar and Martial Hebert
Conference Paper, Carnegie Mellon University, Tenth IEEE International Conference on Computer Vision (ICCV '05), Vol. 2, pp. 1284 - 1291, October, 2005

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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.

BibTeX Reference
@conference{Kumar-2005-9332,
title = {A Hierarchical Field Framework for Unified Context-Based Classification},
author = {Sanjiv Kumar and Martial Hebert},
booktitle = {Tenth IEEE International Conference on Computer Vision (ICCV '05)},
keyword = {vision},
school = {Robotics Institute , Carnegie Mellon University},
month = {October},
year = {2005},
volume = {2},
pages = {1284 - 1291},
address = {Pittsburgh, PA},
}
2017-09-13T10:43:08+00:00