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...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Hui Zhang, Sharath R. Cholleti, Sally A. Goldman, Jason E. Fritts
• Keywords: Computer Vision, CVPR 2006, Human-generated Reference Segmentation, Machine Learning Algorithm, Segmentation Algorithm, Stand-alone Evaluation Methods, Stand-alone Segmentation Evaluation