Fast pseudo-semantic segmentation for joint region-based hierarchical and multiresolution representation

Sekkal, Rafiq; Strauss, Clément; Pasteau, François; Babel, Marie; Déforges, Olivier

doi:10.1117/12.908829

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…to Sekkal et al [11], the regions need to remain coherent with the original content and a better detection of contours leads to an efficient pseudo-semantic representation.…”

Section: Open-set Segmentation Methodsmentioning

confidence: 99%

FuSC: Fusing Superpixels for Improved Semantic Consistency

Nunes,

Pereira,

Oliveira

et al. 2024

IEEE Access

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Open-set segmentation has caught the community's attention only in the last few years, and it is a growing and active research area with many challenges ahead. To better identify open-set pixels, we address two known issues by improving data representation and ensuring semantic consistency in open-set predictions. First, we present a method called Open Gaussian Mixture of Models (OpenGMM) that allows for multimodal statistical distributions in known class pixels using a Gaussian Mixture of Models instead of unimodal approaches, like Principal Component Analysis. The second approach improved semantic consistency by applying a post-processing technique that uses superpixels to enforce homogeneous regions to have similar predictions, rectifying erroneously classified pixels within these regions and providing better delineation of object borders. We also developed a novel superpixel method called Fusing Superpixels for Improved Semantic Consistency (FuSC) that produced more homogeneous superpixels and enhanced, even more, the open-set segmentation prediction. We applied the proposed approaches to well-known remote sensing datasets with labeled ground truth for semantic segmentation tasks. The proposed methods improved the highest AUROC quantitative results for the ISPRS Vaihingen and Potsdam datasets. Using FuSC, we achieved novel open-set state-of-the-art results for both datasets, improving AUROC results from 0.850 to 0.880 (3.53%) for Vaihingen and 0.764 to 0.797 (4.32%) for Potsdam datasets. The official implementation is available at: https://github.com/iannunes/FuSC

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“…to Sekkal et al [11], the regions need to remain coherent with the original content and a better detection of contours leads to an efficient pseudo-semantic representation.…”

Section: Open-set Segmentation Methodsmentioning

confidence: 99%

FuSC: Fusing Superpixels for Improved Semantic Consistency

Nunes,

Pereira,

Oliveira

et al. 2024

IEEE Access

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“…To enable the use of SSeg models in practical situations, it is crucial to improve the semantic and spatial consistency of the resultant segmentation. According to Sekkal et al [11], it is important for the regions to remain coherent with the original content and the better detection of contours leads to an efficient pseudo-semantic representation.…”

Section: Introductionmentioning

confidence: 99%

FuSS: Fusing Superpixels for Improved Segmentation Consistency

Nunes¹,

Pereira²,

Oliveira³

et al. 2022

Preprint

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In this work, we propose two different approaches to improve the semantic consistency of Open Set Semantic Segmentation. First, we propose a method called OpenGMM that extends the OpenPCS framework using a Gaussian Mixture of Models to model the distribution of pixels for each class in a multimodal manner. The second approach is a post-processing which uses superpixels to enforce highly homogeneous regions to behave equally, rectifying erroneous classified pixels within these regions, we also proposed a novel superpixel method called FuSS. All tests were performed on ISPRS Vaihingen and Potsdam datasets, and both methods were capable to improve quantitative and qualitative results for both datasets. Besides that, the post-process with FuSS achieved state-of-the-art results for both datasets. The official implementation is available at: https://github.com/iannunes/FuSS.

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