Abstract
A method for evaluating image segmentation methods is proposed in this paper. The method is based on a perception model where the drawing act is used to represent visual mental percepts. Each segmented image is represented by a minimal set of features and the segmentation method is tested against a set of sketches that represent a subset of the original image database, using the Mahalanobis distance function. The covariance matrix is set using a collection of sketches drawn by different users. The different drawings are demonstrated to be consistent across users. This evaluation method can be used to solve the problem of parameter selection in image segmentation, as well as to show the goodness or limitations of the different segmentation algorithms. Different well-known color segmentation algorithms are analyzed with the proposed method and the nature of each one is discussed. This evaluation method is also compared with heuristic functions that serve for the same purpose, showing the importance of using users' pictorial knowledge.
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