A Novel Fusion Approach Based on the Global Consistency Criterion to Fusing Multiple Segmentations
提出一种基于全局一致性误差的融合模型，将多个快速分割结果融合成更优的分割图，在伯克利数据集上验证了有效性。
In this paper, we introduce a new fusion model whose objective is to fuse multiple region-based segmentation maps to get a final better segmentation result. The suggested 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 GCE criterion with which the fusion of basic, rapidly-computed segmentation results appears as a relevant alternative compared with other (possibly complex) 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 (manual ground truth segmentations). The obtained results clearly demonstrate the efficiency of this fusion model.