Fuzzy Superpixel-based Image Segmentation

This article presents a multi-phase image segmentation methodology based on fuzzy superpixel decom-position, aggregation and merging. First, a collection of layers of dense fuzzy superpixels is generated by the variational fuzzy decomposition algorithm. Then a layer of refined superpixels is extracted by aggre-gating various layers of dense fuzzy superpixels using the hierarchical normalized cuts. Finally, the refined superpixels are projected into the low dimensional feature spaces by the multidimensional scaling and the segmentation result is obtained via the mean-shift-based merging approach with the spatial band-width adjustment strategy. Our algorithm utilizes the superimposition of fuzzy superpixels to impose more accurate spatial constraints on the final segmentation through the fuzzy superpixel aggregation. The fuzziness of superpixels also provides spatial features to measure affinities between fuzzy superpix-els and refined superpixels, and guide the merging process. Comparative experiments with the existing approaches reveal a superior performance of the proposed method.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This article presents a multi-phase image segmentation methodology based on fuzzy superpixel decomposition, aggregation, and merging. The proposed method achieves more accurate segmentation results through hierarchical aggregation of superpixels and multidimensional scaling. Comparative experiments demonstrate the superior performance of the proposed method compared to existing approaches.