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.

翻译：这项工作展示了一种基于超像素分解的区域增长图像分解方法。 从输入图像的初始等离子( control- constricted over section), 图像分化是通过将相似的超像素迭接到各个区域来实现的。 这个方法提出了两个关键问题:(1) 如何计算超像素之间的相似性, 以便进行准确的合并, (2) 将这些超级像素合并为顺序。 从这个角度看, 我们首先采用了一种强大的适应性多级超像素相似性, 在内容和共同边界级别上进行区域比较。 其次, 我们提出了一个全球合并战略, 以有效指导区域合并进程。 这种战略使用一个配对合并标准, 以确保给最佳区域汇总以最高的优先次序。 这样可以将最终的分解到具有强烈边界坚持度的一致区域。 我们在 BSDS500 图像数据集上进行实验, 以突出我们的方法与其他广为人知的图像分解算法的优劣度。 所获得的结果显示了拟议方法的前景。