  This work presents a region-growing image segmentation approach based on
superpixel decomposition. From an initial contour-constrained over-segmentation
of the input image, the image segmentation is achieved by iteratively merging
similar superpixels into regions. This approach raises two key issues: (1) how
to compute the similarity between superpixels in order to perform accurate
merging and (2) in which order those superpixels must be merged together. In
this perspective, we firstly introduce a robust adaptive multi-scale superpixel
similarity in which region comparisons are made both at content and common
border level. Secondly, we propose a global merging strategy to efficiently
guide the region merging process. Such strategy uses an adpative merging
criterion to ensure that best region aggregations are given highest priorities.
This allows to reach a final segmentation into consistent regions with strong
boundary adherence. We perform experiments on the BSDS500 image dataset to
highlight to which extent our method compares favorably against other
well-known image segmentation algorithms. The obtained results demonstrate the
promising potential of the proposed approach.
