Evaluation of distances between color image segmentations
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
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In this paper, a graph partitioning greedy algorithm is presented. This algorithm avoids the hard-constraints of others similar approaches such as the impossibility for some regions to grow after certain step of the algorithm and the uniqueness of the solution. Nevertheless, it allows attaining global results by local approximations using a generalized concept of not over-segmentation, which includes an energy function, and eliminating the not sub-segmentation criterion using a probabilistic criterion similar to that of annealing. The high-variability region problems such as borders are also eliminated identifying them and distributing their pixels among the other neighbor regions. Thus, it is possible to keep the time complexity of usual graph partitioning greedy algorithm and avoiding its high-variability region problems, obtaining better results.