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We present a novel segmentation algorithm based on a hierarchicalrepresentation of images. The main contribution of this work is to explore thecapabilities of the A Contrario reasoning when applied to the segmentationproblem, and to overcome the limitations of current algorithms within thatframework. This exploratory approach has three main goals.  Our first goal is to extend the search space of greedy merging algorithms tothe set of all partitions spanned by a certain hierarchy, and to cast thesegmentation as a selection problem within this space. In this way we increasethe number of tested partitions and thus we potentially improve thesegmentation results. In addition, this space is considerably smaller than thespace of all possible partitions, thus we still keep the complexity controlled.  Our second goal aims to improve the locality of region merging algorithms,which usually merge pairs of neighboring regions. In this work, we overcomethis limitation by introducing a validation procedure for complete partitions,rather than for pairs of regions.  The third goal is to perform an exhaustive experimental evaluationmethodology in order to provide reproducible results.  Finally, we embed the selection process on a statistical A Contrarioframework which allows us to have only one free parameter related to thedesired scale.

A Contrario Selection of Optimal Partitions for Image Segmentation