A Measure for Objective Evaluation of Image Segmentation Algorithms

Despite significant advances in image segmentation techniques, evaluation of these techniques thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images and is otherwise left to subjective evaluation by the...

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Bibliographic Details
Main Authors: Unnikrishnan, R., Pantofaru, C., Hebert, M.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Humans
Image databases
Image segmentation
Machine vision
Maximum likelihood detection
Maximum likelihood estimation
Robots
Shape
Target tracking
Online Access:Request full text
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Summary:Despite significant advances in image segmentation techniques, evaluation of these techniques thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images and is otherwise left to subjective evaluation by the reader. Little effort has been spent on the design of perceptually correct measures to compare an automatic segmentation of an image to a set of hand-segmented examples of the same image. This paper demonstrates how a modification of the Rand index, the Normalized Probabilistic Rand (NPR) index, meets the requirements of largescale performance evaluation of image segmentation. We show that the measure has a clear probabilistic interpretation as the maximum likelihood estimator of an underlying Gibbs model, can be correctly normalized to account for the inherent similarity in a set of ground truth images, and can be computed efficiently for large datasets. Results are presented on images from the publicly available Berkeley Segmentation dataset.
ISSN:2160-7508
2160-7516
DOI:10.1109/CVPR.2005.390