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CVPR
2006
IEEE

Meta-Evaluation of Image Segmentation Using Machine Learning

14 years 5 months ago
Meta-Evaluation of Image Segmentation Using Machine Learning
Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation algorithm, or parameterization of a given algorithm, is selected at the application level and fixed for all images within that application. Our goal is to create a stand-alone method to evaluate segmentation quality. Stand-alone methods have the advantage that they do not require a manually-segmented reference image for comparison, and can therefore be used for real-time evaluation. Current stand-alone evaluation methods often work well for some types of images, but poorly for others. We propose a meta-evaluation method in which any set of base evaluation methods are combined by a machine learning algorithm that coalesces their evaluations based on a learned weighting function, which depends upon the image to be segmented. The training data used by the machine learning algorithm can be labeled by a human, based on similarity to a human-generated reference segmentation...
Hui Zhang, Sharath R. Cholleti, Sally A. Goldman,
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2006
Where CVPR
Authors Hui Zhang, Sharath R. Cholleti, Sally A. Goldman, Jason E. Fritts
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