Adaptive strategy for superpixel-based region-growing image segmentation

TL;DR

This paper introduces an adaptive superpixel-based region-growing method for image segmentation that improves merging accuracy through multi-scale similarity measures and a global merging strategy, achieving competitive results on BSDS500.

Contribution

The paper proposes a novel adaptive multi-scale superpixel similarity measure and a global merging strategy for improved region-growing image segmentation.

Findings

Method compares favorably against existing algorithms.

Achieves accurate boundary adherence in segmentation.

Demonstrates promising results on BSDS500 dataset.

Abstract

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.