GCE-BASED MODEL FOR THE FUSION OF MULTIPLES COLOR IMAGE SEGMENTATIONS

In this work, we introduce a new fusion model whose objective is to fuse multiple region-based segmentation maps to get a final better segmentation result. This new fusion model is based on an energy function originated from the global consistency error (GCE), a perceptual measure which takes into account the inherent multiscale nature of an image segmentation by measuring the level of refinement existing between two spatial partitions. Combined with a region merging/splitting prior, this new energy-based fusion model of label fields allows to define an interesting penalized likelihood estimation procedure based on the global consistency error criterion with which the fusion of basic, rapidly-computed segmentation results appears as a relevant alternative compared with other segmentation techniques proposed in the image segmentation field. The performance of our fusion model was evaluated on the Berkeley dataset including various segmentations given by humans.