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Retrieving images using object shapes usually requires to specify the major (or salient) structures in the image. Detecting salient structures is a basic task in perceptual organization. Saliency algorithms depend on grouping cues and mark edge-points with some saliency measure, which typically grows with the length and smoothness of the curve on which these edge-points lie. We consider a particular saliency mechanism  and focus on one aspect of its design: how different image measurements should be combined to get an effective grouping cue, leading to better specifications of the salient structures. We discuss 5 different combination methods, based, respectively, on a common heuristic, on Bayesian considerations (two versions), on optimizing a figure-ground separation measure using recent analysis results, and on incorporation of global information using bootstrap. Three of these cues are refinements of older methods and two are completely new. The advantage of the later methods is demonstrated.