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Segmentation by Blended Partitional Clustering for Different Color Spaces

This paper presents a new segmentation strategy, based on a blended procedure whose goal is to combine several segmentation maps in order to finally get a more reliable and accurate segmentation result. The fusion strategy aims at combining these segmentation maps with a final clustering procedure using as input features, the local histogram of the class labels, previously estimated and associated to each site and for all these initial partitions. The different label fields to be fused are given by the same and simple K-means based (blended partitional) clustering technique on an input image expressed in different color spaces. This fusion framework remains simple to implement, fast, general enough to be applied to various computer vision applications.