A Probabilistic Interpretation of the Saliency Network
نویسندگان
چکیده
The calculation of salient structures is one of the early and basic ideas of perceptual organization in Computer Vision. Saliency algorithms aim to nd image curves, maximizing some deterministic quality measure which grows with the length of the curve, its smoothness, and its continuity. This note proposes a modiied saliency estimation mechanism, which is based on probabilistically speciied grouping cue and on length estimation. In the context of the proposed method, the well known saliency mechanism, proposed by Shaashua and Ullman SU88] may be interpreted as a process trying to detect the curve with maximal expected length. Besides giving a new interpretation and a principled justiication to older measures, the proposed saliency mechanism is able to use diierent grouping cues and thus generalizes the scope of saliency detection to other domains, in a systematic rigorous way.
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