14 September 2017 Adaptive strategy for superpixel-based region-growing image segmentation
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This work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained oversegmentation 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 first introduce a robust adaptive multiscale superpixel similarity in which region comparisons are made both at content and common border level. Second, we propose a global merging strategy to efficiently guide the region merging process. Such strategy uses an adaptive merging criterion to ensure that best region aggregations are given highest priorities. This allows the ability 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.
© 2017 SPIE and IS&T
Mahaman Sani Chaibou, Pierre-Henri Conze, Karim Kalti, Basel Solaiman, Mohamed Ali Mahjoub, "Adaptive strategy for superpixel-based region-growing image segmentation," Journal of Electronic Imaging 26(6), 061605 (14 September 2017). https://doi.org/10.1117/1.JEI.26.6.061605 . Submission: Received: 21 April 2017; Accepted: 18 August 2017
Received: 21 April 2017; Accepted: 18 August 2017; Published: 14 September 2017


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