Technical Report CIS9909

Title: A probabilistic interpretation of the Saliency Network
Authors: Michael Lindenbaum and Alexander Berengolts
Abstract: The calculation of salient structures is one of the early and basic ideas of perceptual organization in Computer Vision. Saliency algorithms aim to find image curves, maximizing some deterministic quality measure which grows with the length of the curve, its smoothness, and its continuity. This note proposes a modified saliency estimation mechanism, which is based on probabilistically specified grouping cue and on length estimation. In the context of the proposed method, the well known saliency mechanism, proposed by Shaashua and Ullman may be interpreted as a process trying to detect the curve with maximal expected length. Besides giving a new interpretation and a principled justification to older measures, the proposed saliency mechanism is able to use different grouping cues and thus generalizes the scope of saliency detection to other domains, in a systematic rigorous way.
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